From d7e430785f31a702a52f80164518c7340b578801 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 22 Jan 2026 17:34:16 +0100
Subject: [PATCH 01/69] Added benchmarking code and results, added fast DPP
 path

---
 benchmarks/README.md                          |  141 ++
 benchmarks/__init__.py                        |    5 +
 benchmarks/__main__.py                        |  144 ++
 benchmarks/core/__init__.py                   |   22 +
 benchmarks/core/config.py                     |   41 +
 benchmarks/core/data.py                       |  185 ++
 benchmarks/core/embeddings.py                 |   90 +
 benchmarks/core/metrics.py                    |   50 +
 benchmarks/core/report.py                     |  485 ++++
 benchmarks/core/runner.py                     |  194 ++
 benchmarks/results/RESULTS.md                 |  222 ++
 .../amazon-product-reviews-video-games.json   |  351 +++
 benchmarks/results/goodreads-rating.json      |  351 +++
 benchmarks/results/lastfm.json                |  351 +++
 benchmarks/results/latency.png                |  Bin 0 -> 53977 bytes
 benchmarks/results/ml-32m.json                |  351 +++
 benchmarks/results/pareto.png                 |  Bin 0 -> 334205 bytes
 pyproject.toml                                |   17 +
 src/pyversity/strategies/dpp.py               |   13 +
 uv.lock                                       | 2048 ++++++++++++++++-
 20 files changed, 5057 insertions(+), 4 deletions(-)
 create mode 100644 benchmarks/README.md
 create mode 100644 benchmarks/__init__.py
 create mode 100644 benchmarks/__main__.py
 create mode 100644 benchmarks/core/__init__.py
 create mode 100644 benchmarks/core/config.py
 create mode 100644 benchmarks/core/data.py
 create mode 100644 benchmarks/core/embeddings.py
 create mode 100644 benchmarks/core/metrics.py
 create mode 100644 benchmarks/core/report.py
 create mode 100644 benchmarks/core/runner.py
 create mode 100644 benchmarks/results/RESULTS.md
 create mode 100644 benchmarks/results/amazon-product-reviews-video-games.json
 create mode 100644 benchmarks/results/goodreads-rating.json
 create mode 100644 benchmarks/results/lastfm.json
 create mode 100644 benchmarks/results/latency.png
 create mode 100644 benchmarks/results/ml-32m.json
 create mode 100644 benchmarks/results/pareto.png

diff --git a/benchmarks/README.md b/benchmarks/README.md
new file mode 100644
index 0000000..f741e82
--- /dev/null
+++ b/benchmarks/README.md
@@ -0,0 +1,141 @@
+# Pyversity Benchmarks
+
+Comprehensive evaluation of diversification strategies across 4 recommendation datasets.
+We measure the relevance-diversity tradeoff to help you choose the right strategy for your use case.
+
+## Key Finding
+
+**Different strategies excel at different diversity levels:**
+
+| Diversity Level | Winner | Recommendation |
+|-----------------|--------|----------------|
+| Low (ILAD 0.3-0.5) | **SSD** | Best for light diversification |
+| Moderate (ILAD 0.5-0.7) | **MSD/DPP** | Good middle-ground |
+| High (ILAD 0.7-0.9) | **MSD** | Best for heavy diversification |
+| Maximum (ILAD 0.9+) | **MSD** | Dominates at extreme diversity |
+
+![Relevance vs Diversity Tradeoff](results/pareto.png)
+
+## Quick Recommendations
+
+| Your Goal | Strategy | λ | Notes |
+|-----------|----------|---|-------|
+| Light diversification | **SSD** | 0.6-0.8 | Highest relevance at moderate diversity |
+| Balanced tradeoff | **DPP** | 0.4-0.6 | Good across multiple diversity levels |
+| Maximum diversity | **MSD** | 0.4-0.6 | Best when diversity is the priority |
+| Speed-critical | **MMR** | 0.6-0.8 | Fastest (O(k·n) complexity) |
+
+## Usage
+
+```bash
+# Download datasets
+python -m benchmarks download
+
+# Run benchmarks
+python -m benchmarks run
+
+# Generate report
+python -m benchmarks report
+```
+
+<details>
+<summary>Detailed Results</summary>
+
+### Datasets
+
+| Dataset | Domain | Interactions | Source |
+|---------|--------|--------------|--------|
+| MovieLens-32M | Movies | 32M ratings | [GroupLens](https://grouplens.org/datasets/movielens/32m/) |
+| Last.FM | Music | 92K plays | [HetRec 2011](http://ir.ii.uam.es/hetrec2011/) |
+| Amazon Video Games | Games | 47K reviews | [UCSD](https://cseweb.ucsd.edu/~jmcauley/datasets.html) |
+| Goodreads | Books | 869K ratings | [UCSD](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/) |
+
+### Strategies
+
+| Strategy | Description | Complexity |
+|----------|-------------|------------|
+| **MMR** | Maximal Marginal Relevance | O(k·n) |
+| **MSD** | Max-Sum Diversification | O(k·n) |
+| **DPP** | Determinantal Point Process | O(k²·n) |
+| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
+
+### Metrics
+
+| Metric | Type | Description |
+|--------|------|-------------|
+| nDCG@10 | Relevance | Normalized Discounted Cumulative Gain |
+| MRR | Relevance | Mean Reciprocal Rank |
+| ILAD | Diversity | Intra-List Average Distance |
+| Latency | Efficiency | Time per diversification call |
+
+### Per-Dataset Winners
+
+| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Max (0.9+) |
+|---------|:-------------:|:------------------:|:--------------:|:----------:|
+| MovieLens-32M | SSD | DPP | MSD | - |
+| Last.FM | SSD | MSD | MSD | MSD |
+| Amazon-VG | - | - | MMR | MSD |
+| Goodreads | SSD | MSD | MSD | MSD |
+
+### Methodology
+
+- **Evaluation**: Leave-one-out protocol with 2,000 sampled users per dataset
+- **Embeddings**: 64-dim SVD on item co-occurrence matrix
+- **Candidates**: Top-100 similar items per profile item
+- **Selection**: k=20 items selected by each strategy
+- **λ sweep**: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0
+
+### Full Results
+
+See [`results/RESULTS.md`](results/RESULTS.md) for complete tables.
+
+</details>
+
+<details>
+<summary>Programmatic API</summary>
+
+```python
+from benchmarks import BenchmarkConfig, run_benchmark
+from pyversity import Strategy
+
+config = BenchmarkConfig(
+    dataset_path="local/data/ml-32m",
+    sample_users=1000,
+    strategies=[Strategy.MMR, Strategy.DPP, Strategy.MSD, Strategy.SSD],
+    diversity_values=[0.0, 0.3, 0.5, 0.7, 1.0],
+)
+results = run_benchmark(config)
+```
+
+</details>
+
+<details>
+<summary>Citations</summary>
+
+```bibtex
+@article{harper2015movielens,
+  title={The MovieLens Datasets: History and Context},
+  author={Harper, F Maxwell and Konstan, Joseph A},
+  journal={ACM TiiS}, year={2015}
+}
+
+@inproceedings{cantador2011hetrec,
+  title={2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems},
+  author={Cantador, Iv{\'a}n and Brusilovsky, Peter and Kuflik, Tsvi},
+  booktitle={RecSys}, year={2011}
+}
+
+@inproceedings{ni2019amazon,
+  title={Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects},
+  author={Ni, Jianmo and Li, Jiacheng and McAuley, Julian},
+  booktitle={EMNLP}, year={2019}
+}
+
+@inproceedings{wan2018goodreads,
+  title={Item Recommendation on Monotonic Behavior Chains},
+  author={Wan, Mengting and McAuley, Julian},
+  booktitle={RecSys}, year={2018}
+}
+```
+
+</details>
diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py
new file mode 100644
index 0000000..2413af1
--- /dev/null
+++ b/benchmarks/__init__.py
@@ -0,0 +1,5 @@
+"""Pyversity benchmarking suite."""
+
+from benchmarks.core import BenchmarkConfig, run_benchmark
+
+__all__ = ["BenchmarkConfig", "run_benchmark"]
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
new file mode 100644
index 0000000..ab31168
--- /dev/null
+++ b/benchmarks/__main__.py
@@ -0,0 +1,144 @@
+"""
+CLI entry point for benchmarks.
+
+Usage:
+    python -m benchmarks download   # Download datasets
+    python -m benchmarks run        # Run benchmarks on all datasets
+    python -m benchmarks report     # Generate markdown report + plots
+    python -m benchmarks            # Run all (default)
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import zipfile
+from pathlib import Path
+
+import requests
+from tqdm import tqdm
+
+from benchmarks.core import (
+    DATASET_REGISTRY,
+    BenchmarkConfig,
+    generate_report,
+    run_benchmark,
+)
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(message)s",
+)
+logger = logging.getLogger(__name__)
+
+RESULTS_DIR = Path("benchmarks/results")
+DATA_DIR = Path("local/data")
+
+
+def cmd_download() -> None:
+    """Download datasets from original sources."""
+    logger.info("Downloading benchmark datasets...")
+
+    for name, info in DATASET_REGISTRY.items():
+        if info.download_url is None:
+            continue  # HuggingFace datasets auto-download
+
+        dest_dir = Path(info.path)
+
+        if dest_dir.exists() and any(dest_dir.iterdir()):
+            logger.debug(f"{name} already exists, skipping")
+            continue
+
+        logger.info(f"Downloading {name}...")
+        try:
+            DATA_DIR.mkdir(parents=True, exist_ok=True)
+            zip_path = DATA_DIR / f"{name}.zip"
+
+            # Download
+            response = requests.get(info.download_url, stream=True, timeout=30)
+            response.raise_for_status()
+            total = int(response.headers.get("content-length", 0))
+
+            with open(zip_path, "wb") as f, tqdm(total=total, unit="B", unit_scale=True) as pbar:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+                    pbar.update(len(chunk))
+
+            # Extract
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                zf.extractall(DATA_DIR)
+            zip_path.unlink()
+
+            # Handle nested directories
+            for d in DATA_DIR.iterdir():
+                if d.is_dir() and d != dest_dir:
+                    if name in d.name or (name == "lastfm" and "hetrec" in d.name.lower()):
+                        d.rename(dest_dir)
+                        break
+
+            logger.info(f"Saved {name} to {dest_dir}")
+
+        except Exception as e:
+            logger.error(f"Failed to download {name}: {e}")
+
+    logger.info("Download complete")
+
+
+def cmd_run() -> None:
+    """Run benchmarks on all datasets."""
+    logger.info("Running benchmark suite...")
+
+    for name, info in DATASET_REGISTRY.items():
+        logger.info(f"Benchmarking {name}...")
+
+        config = BenchmarkConfig(
+            dataset=name,
+            rating_threshold=info.rating_threshold,
+        )
+
+        try:
+            run_benchmark(config)
+        except FileNotFoundError as e:
+            logger.warning(f"Skipping {name}: {e} (run: python -m benchmarks download)")
+        except Exception as e:
+            logger.error(f"Error on {name}: {e}")
+
+    logger.info("Benchmark complete")
+
+
+def cmd_report() -> None:
+    """Generate markdown report and plots."""
+    generate_report(RESULTS_DIR)
+
+
+def main() -> None:
+    """Main CLI entry point."""
+    parser = argparse.ArgumentParser(
+        description="Pyversity Benchmark Suite",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "command",
+        nargs="?",
+        choices=["download", "run", "report"],
+        default=None,
+        help="Command to run (default: run)",
+    )
+
+    args = parser.parse_args()
+
+    if args.command == "download":
+        cmd_download()
+    elif args.command == "run":
+        cmd_run()
+    elif args.command == "report":
+        cmd_report()
+    else:
+        # Default: run
+        cmd_run()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/core/__init__.py b/benchmarks/core/__init__.py
new file mode 100644
index 0000000..05fb9d3
--- /dev/null
+++ b/benchmarks/core/__init__.py
@@ -0,0 +1,22 @@
+"""Core benchmark modules."""
+
+from benchmarks.core.config import BenchmarkConfig
+from benchmarks.core.data import DATASET_REGISTRY, DatasetInfo, DatasetType, InteractionData, load_dataset
+from benchmarks.core.metrics import ilad, ilmd, mrr, ndcg
+from benchmarks.core.report import generate_report
+from benchmarks.core.runner import run_benchmark
+
+__all__ = [
+    "BenchmarkConfig",
+    "DATASET_REGISTRY",
+    "DatasetInfo",
+    "DatasetType",
+    "InteractionData",
+    "generate_report",
+    "ilad",
+    "ilmd",
+    "load_dataset",
+    "mrr",
+    "ndcg",
+    "run_benchmark",
+]
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
new file mode 100644
index 0000000..98699f5
--- /dev/null
+++ b/benchmarks/core/config.py
@@ -0,0 +1,41 @@
+"""Benchmark configuration."""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from pyversity import Strategy
+
+
+@dataclass
+class BenchmarkConfig:
+    """Configuration for benchmark runs."""
+
+    # Dataset (name from registry or DatasetInfo)
+    dataset: str | None = None
+    min_interactions: int = 5
+    rating_threshold: float | None = None  # None = use dataset default
+
+    # Embeddings
+    embedding_dim: int = 64
+
+    # Candidate generation
+    topk_similar_per_item: int = 50
+    max_candidates: int = 1000
+
+    # Evaluation
+    k: int = 20
+    sample_users: int = 2000
+
+    # Strategies
+    strategies: list[Strategy] = field(default_factory=lambda: [Strategy.MMR, Strategy.MSD, Strategy.DPP, Strategy.SSD])
+    diversity_values: list[float] = field(default_factory=lambda: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+
+    # Output
+    output_dir: Path = Path("benchmarks/results")
+
+    # Reproducibility
+    seed: int = 42
+
+    def __post_init__(self) -> None:
+        """Convert paths to Path objects."""
+        self.output_dir = Path(self.output_dir)
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
new file mode 100644
index 0000000..a486f01
--- /dev/null
+++ b/benchmarks/core/data.py
@@ -0,0 +1,185 @@
+"""Data loading and preprocessing."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+logger = logging.getLogger(__name__)
+
+
+class DatasetType(Enum):
+    """Known dataset types."""
+
+    MOVIELENS = "movielens"
+    LASTFM = "lastfm"
+    HUGGINGFACE = "huggingface"
+
+
+@dataclass
+class DatasetInfo:
+    """Dataset configuration."""
+
+    name: str
+    path: str  # Local path or hf://... identifier
+    dataset_type: DatasetType
+    rating_threshold: float
+    download_url: str | None = None
+
+
+# Registry of known datasets
+DATASET_REGISTRY: dict[str, DatasetInfo] = {
+    "ml-32m": DatasetInfo(
+        name="ml-32m",
+        path="local/data/ml-32m",
+        dataset_type=DatasetType.MOVIELENS,
+        rating_threshold=4.0,
+        download_url="https://files.grouplens.org/datasets/movielens/ml-32m.zip",
+    ),
+    "lastfm": DatasetInfo(
+        name="lastfm",
+        path="local/data/lastfm",
+        dataset_type=DatasetType.LASTFM,
+        rating_threshold=1.0,
+        download_url="http://files.grouplens.org/datasets/hetrec2011/hetrec2011-lastfm-2k.zip",
+    ),
+    "amazon-video-games": DatasetInfo(
+        name="amazon-video-games",
+        path="hf://LoganKells/amazon_product_reviews_video_games",
+        dataset_type=DatasetType.HUGGINGFACE,
+        rating_threshold=4.0,
+    ),
+    "goodreads": DatasetInfo(
+        name="goodreads",
+        path="hf://qmaruf/goodreads-rating",
+        dataset_type=DatasetType.HUGGINGFACE,
+        rating_threshold=4.0,
+    ),
+}
+
+
+@dataclass
+class InteractionData:
+    """Preprocessed interaction data."""
+
+    user_ids: NDArray[np.int64]
+    item_ids: NDArray[np.int64]
+    n_users: int
+    n_items: int
+
+
+def load_dataset(
+    dataset: str | DatasetInfo,
+    min_interactions: int = 5,
+    rating_threshold: float | None = None,
+) -> InteractionData:
+    """
+    Load and preprocess a dataset.
+
+    Args:
+    ----
+        dataset: Dataset name (from registry) or DatasetInfo object
+        min_interactions: Minimum interactions per user AND item
+        rating_threshold: Override rating threshold (None = use dataset default)
+
+    Returns:
+    -------
+        Preprocessed InteractionData
+
+    """
+    # Resolve dataset info
+    if isinstance(dataset, str):
+        if dataset not in DATASET_REGISTRY:
+            raise ValueError(f"Unknown dataset: {dataset}. Known: {list(DATASET_REGISTRY.keys())}")
+        info = DATASET_REGISTRY[dataset]
+    else:
+        info = dataset
+
+    threshold = rating_threshold if rating_threshold is not None else info.rating_threshold
+
+    # Load based on type
+    if info.dataset_type == DatasetType.HUGGINGFACE:
+        df = _load_huggingface(info.path.replace("hf://", ""))
+    elif info.dataset_type == DatasetType.MOVIELENS:
+        df = _load_movielens(Path(info.path))
+    elif info.dataset_type == DatasetType.LASTFM:
+        df = _load_lastfm(Path(info.path))
+    else:
+        raise ValueError(f"Unknown dataset type: {info.dataset_type}")
+
+    logger.debug(f"Loaded {len(df):,} raw interactions")
+
+    # Binarize
+    if threshold is not None:
+        df = df[df["value"] >= threshold].copy()
+        logger.debug(f"After binarization (>= {threshold}): {len(df):,}")
+
+    # Filter by interaction count (iteratively until stable)
+    prev_len = 0
+    while len(df) != prev_len:
+        prev_len = len(df)
+        user_counts = df["user"].value_counts()
+        item_counts = df["item"].value_counts()
+        df = df[df["user"].isin(user_counts[user_counts >= min_interactions].index)]
+        df = df[df["item"].isin(item_counts[item_counts >= min_interactions].index)]
+
+    logger.debug(f"After filtering (min {min_interactions}): {len(df):,}")
+
+    # Remap IDs to contiguous integers
+    df["user"] = pd.factorize(df["user"])[0]
+    df["item"] = pd.factorize(df["item"])[0]
+
+    n_users = df["user"].nunique()
+    n_items = df["item"].nunique()
+    logger.debug(f"Final: {n_users:,} users, {n_items:,} items, {len(df):,} interactions")
+
+    return InteractionData(
+        user_ids=df["user"].values.astype(np.int64),
+        item_ids=df["item"].values.astype(np.int64),
+        n_users=n_users,
+        n_items=n_items,
+    )
+
+
+def _load_movielens(path: Path) -> pd.DataFrame:
+    """Load MovieLens dataset."""
+    df = pd.read_csv(path / "ratings.csv")
+    return df.rename(columns={"userId": "user", "movieId": "item", "rating": "value"})[["user", "item", "value"]]
+
+
+def _load_lastfm(path: Path) -> pd.DataFrame:
+    """Load Last.FM HetRec 2011 dataset."""
+    df = pd.read_csv(path / "user_artists.dat", sep="\t")
+    return df.rename(columns={"userID": "user", "artistID": "item", "weight": "value"})[["user", "item", "value"]]
+
+
+def _load_huggingface(dataset_id: str) -> pd.DataFrame:
+    """Load a dataset from HuggingFace Hub."""
+    from datasets import load_dataset as hf_load_dataset
+
+    logger.debug(f"Loading from HuggingFace: {dataset_id}")
+    ds = hf_load_dataset(dataset_id, split="train")
+    df = ds.to_pandas()
+
+    # Amazon Video Games format
+    if "reviewerID" in df.columns:
+        df = df.rename(columns={"reviewerID": "user", "asin": "item", "overall": "value"})
+        df = df[df["value"] > 0]
+        df["user"] = pd.factorize(df["user"])[0]
+        df["item"] = pd.factorize(df["item"])[0]
+        return df[["user", "item", "value"]]
+
+    # Goodreads format
+    if "user_id" in df.columns and "book_id" in df.columns:
+        df = df.rename(columns={"user_id": "user", "book_id": "item", "rating": "value"})
+        df = df[df["value"] > 0]
+        df["user"] = pd.factorize(df["user"])[0]
+        return df[["user", "item", "value"]]
+
+    raise ValueError(f"Unknown HuggingFace dataset format: {list(df.columns)}")
diff --git a/benchmarks/core/embeddings.py b/benchmarks/core/embeddings.py
new file mode 100644
index 0000000..da094c6
--- /dev/null
+++ b/benchmarks/core/embeddings.py
@@ -0,0 +1,90 @@
+"""Embedding generation and similarity computation."""
+
+import logging
+
+import numpy as np
+from numpy.typing import NDArray
+from scipy import sparse
+from sklearn.decomposition import TruncatedSVD
+from sklearn.preprocessing import normalize
+
+from benchmarks.core.data import InteractionData
+
+logger = logging.getLogger(__name__)
+
+
+def generate_embeddings(data: InteractionData, dim: int = 64, seed: int = 42) -> NDArray[np.float32]:
+    """
+    Generate item embeddings using SVD on the user-item matrix.
+
+    Returns L2-normalized embeddings of shape (n_items, dim).
+    """
+    ui_matrix = sparse.coo_matrix(
+        (np.ones(len(data.user_ids)), (data.user_ids, data.item_ids)),
+        shape=(data.n_users, data.n_items),
+        dtype=np.float32,
+    ).tocsr()
+
+    effective_dim = min(dim, min(ui_matrix.T.shape) - 1)
+    svd = TruncatedSVD(n_components=effective_dim, random_state=seed)
+    embeddings = svd.fit_transform(ui_matrix.T)
+    embeddings = normalize(embeddings, norm="l2", axis=1)
+
+    logger.debug(f"Embeddings: {embeddings.shape}, explained variance: {svd.explained_variance_ratio_.sum():.3f}")
+    return embeddings.astype(np.float32)
+
+
+def compute_similarity_matrix(embeddings: NDArray[np.float32], top_k: int = 100) -> sparse.csr_matrix:
+    """Compute sparse item-item similarity matrix (top-k per item)."""
+    n_items = embeddings.shape[0]
+    similarity = embeddings @ embeddings.T
+
+    rows, cols, data = [], [], []
+    for i in range(n_items):
+        sims = similarity[i].copy()
+        sims[i] = -np.inf  # Exclude self
+
+        if top_k < n_items - 1:
+            top_idx = np.argpartition(sims, -top_k)[-top_k:]
+        else:
+            top_idx = np.arange(n_items)[np.arange(n_items) != i]
+
+        for j in top_idx:
+            if sims[j] > 0:
+                rows.append(i)
+                cols.append(j)
+                data.append(sims[j])
+
+    return sparse.coo_matrix((data, (rows, cols)), shape=(n_items, n_items), dtype=np.float32).tocsr()
+
+
+def get_candidates(
+    profile_items: NDArray[np.int64],
+    similarity_matrix: sparse.csr_matrix,
+    topk_per_item: int = 50,
+    max_candidates: int = 1000,
+) -> tuple[NDArray[np.int64], NDArray[np.float32]]:
+    """Generate candidate items for a user based on their profile."""
+    profile_set = set(profile_items)
+    scores: dict[int, float] = {}
+
+    for item in profile_items:
+        row = similarity_matrix.getrow(item)
+        indices, similarities = row.indices, row.data
+
+        if len(indices) > topk_per_item:
+            top_idx = np.argpartition(similarities, -topk_per_item)[-topk_per_item:]
+            indices, similarities = indices[top_idx], similarities[top_idx]
+
+        for idx, sim in zip(indices, similarities):
+            if idx not in profile_set:
+                scores[idx] = scores.get(idx, 0.0) + sim
+
+    if not scores:
+        return np.array([], dtype=np.int64), np.array([], dtype=np.float32)
+
+    sorted_items = sorted(scores.items(), key=lambda x: -x[1])[:max_candidates]
+    return (
+        np.array([c[0] for c in sorted_items], dtype=np.int64),
+        np.array([c[1] for c in sorted_items], dtype=np.float32),
+    )
diff --git a/benchmarks/core/metrics.py b/benchmarks/core/metrics.py
new file mode 100644
index 0000000..2630a37
--- /dev/null
+++ b/benchmarks/core/metrics.py
@@ -0,0 +1,50 @@
+"""Evaluation metrics."""
+
+import numpy as np
+from numpy.typing import NDArray
+
+
+def mrr(recommendations: NDArray[np.int64], relevant: NDArray[np.int64]) -> float:
+    """Mean Reciprocal Rank."""
+    relevant_set = set(relevant)
+    for rank, item in enumerate(recommendations, start=1):
+        if item in relevant_set:
+            return 1.0 / rank
+    return 0.0
+
+
+def ndcg(recommendations: NDArray[np.int64], relevant: NDArray[np.int64], k: int = 10) -> float:
+    """Normalized Discounted Cumulative Gain at k."""
+    relevant_set = set(relevant)
+    recommendations = recommendations[:k]
+
+    dcg = sum(1.0 / np.log2(i + 2) for i, item in enumerate(recommendations) if item in relevant_set)
+    idcg = sum(1.0 / np.log2(i + 2) for i in range(min(len(relevant), k)))
+
+    return dcg / idcg if idcg > 0 else 0.0
+
+
+def ilad(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
+    """Intra-List Average Distance (diversity)."""
+    if len(item_ids) < 2:
+        return 0.0
+
+    embs = embeddings[item_ids]
+    sims = embs @ embs.T
+    n = len(item_ids)
+    mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+
+    return float(np.mean(1.0 - sims[mask]))
+
+
+def ilmd(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
+    """Intra-List Minimum Distance (worst-case diversity)."""
+    if len(item_ids) < 2:
+        return 0.0
+
+    embs = embeddings[item_ids]
+    sims = embs @ embs.T
+    n = len(item_ids)
+    mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+
+    return float(np.min(1.0 - sims[mask]))
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
new file mode 100644
index 0000000..8dfcb6a
--- /dev/null
+++ b/benchmarks/core/report.py
@@ -0,0 +1,485 @@
+"""Report generation for benchmark results."""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
+
+
+def _compute_retention_table(results: list[dict], retention_pct: float = 0.95) -> dict:
+    """Compute diversity achieved at given relevance retention threshold."""
+    table: dict[str, dict] = {}
+
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        table[dataset] = {}
+
+        for strategy in STRATEGIES:
+            runs = [r for r in dataset_result["results"] if r["strategy"] == strategy]
+            baseline = next((r for r in runs if r["diversity"] == 0.0), None)
+
+            if not baseline:
+                continue
+
+            threshold = baseline["ndcg@10"] * retention_pct
+            valid = [r for r in runs if r["ndcg@10"] >= threshold and r["diversity"] > 0]
+
+            if valid:
+                best = max(valid, key=lambda r: r["ilad"])
+                table[dataset][strategy] = {
+                    "ilad": best["ilad"],
+                    "lambda": best["diversity"],
+                    "ndcg": best["ndcg@10"],
+                    "retention": best["ndcg@10"] / baseline["ndcg@10"],
+                }
+
+    return table
+
+
+def _compute_regional_winners(results: list[dict]) -> dict:
+    """Compute which strategy wins in each diversity region."""
+    # Define diversity regions
+    regions = {
+        "Low (0.3-0.5)": (0.3, 0.5),
+        "Moderate (0.5-0.7)": (0.5, 0.7),
+        "High (0.7-0.9)": (0.7, 0.9),
+        "Maximum (0.9+)": (0.9, 1.0),
+    }
+
+    # Collect all points
+    all_points = []
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        for r in dataset_result["results"]:
+            all_points.append(
+                {
+                    "dataset": dataset,
+                    "strategy": r["strategy"],
+                    "ndcg": r["ndcg@10"],
+                    "ilad": r["ilad"],
+                }
+            )
+
+    # Find winner for each dataset/region
+    regional_data: dict[str, dict] = {}
+    datasets = sorted(set(p["dataset"] for p in all_points))
+
+    for dataset in datasets:
+        ds_points = [p for p in all_points if p["dataset"] == dataset]
+        regional_data[dataset] = {}
+
+        for region_name, (lo, hi) in regions.items():
+            in_region = [p for p in ds_points if lo <= p["ilad"] < hi]
+            if in_region:
+                best = max(in_region, key=lambda x: x["ndcg"])
+                regional_data[dataset][region_name] = {
+                    "winner": best["strategy"],
+                    "ndcg": best["ndcg"],
+                    "ilad": best["ilad"],
+                }
+
+    # Aggregate wins per region
+    region_wins: dict[str, dict[str, int]] = {r: {} for r in regions}
+    for dataset, regions_dict in regional_data.items():
+        for region_name, data in regions_dict.items():
+            winner = data["winner"]
+            region_wins[region_name][winner] = region_wins[region_name].get(winner, 0) + 1
+
+    return {"per_dataset": regional_data, "aggregate": region_wins}
+
+
+def _compute_f1_scores(results: list[dict]) -> tuple[dict, dict]:
+    """Compute normalized F1 scores (harmonic mean of nDCG and ILAD)."""
+    best_per_dataset: dict[str, dict] = {}
+    all_scores: list[dict] = []
+
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        best_per_dataset[dataset] = {}
+
+        # Get min/max for normalization within dataset
+        ndcgs = [r["ndcg@10"] for r in dataset_result["results"]]
+        ilads = [r["ilad"] for r in dataset_result["results"]]
+        ndcg_min, ndcg_max = min(ndcgs), max(ndcgs)
+        ilad_min, ilad_max = min(ilads), max(ilads)
+
+        for strategy in STRATEGIES:
+            runs = [r for r in dataset_result["results"] if r["strategy"] == strategy and 0 < r["diversity"] < 1]
+
+            best_f1: float = 0.0
+            best_run = None
+
+            for r in runs:
+                ndcg_norm = (r["ndcg@10"] - ndcg_min) / (ndcg_max - ndcg_min) if ndcg_max > ndcg_min else 0
+                ilad_norm = (r["ilad"] - ilad_min) / (ilad_max - ilad_min) if ilad_max > ilad_min else 0
+
+                if ndcg_norm + ilad_norm > 0:
+                    f1 = 2 * ndcg_norm * ilad_norm / (ndcg_norm + ilad_norm)
+                else:
+                    f1 = 0
+
+                if f1 > best_f1:
+                    best_f1 = f1
+                    best_run = r
+
+            if best_run:
+                best_per_dataset[dataset][strategy] = {
+                    "f1": best_f1,
+                    "ndcg": best_run["ndcg@10"],
+                    "ilad": best_run["ilad"],
+                    "lambda": best_run["diversity"],
+                }
+                all_scores.append({"strategy": strategy, "f1": best_f1})
+
+    # Compute average F1 per strategy
+    strategy_avg: dict[str, float] = {}
+    for strategy in STRATEGIES:
+        scores: list[float] = [s["f1"] for s in all_scores if s["strategy"] == strategy]
+        strategy_avg[strategy] = sum(scores) / len(scores) if scores else 0.0
+
+    return best_per_dataset, strategy_avg
+
+
+def _format_retention_table(retention_table: dict) -> list[str]:
+    """Format the retention table as markdown lines."""
+    lines = [
+        "## Diversity at 95% Relevance Retention\n",
+        "How much diversity (ILAD) can each strategy achieve while maintaining ≥95% of baseline nDCG?\n",
+        "| Dataset | MMR | MSD | DPP | SSD | Winner |",
+        "|---------|:---:|:---:|:---:|:---:|:------:|",
+    ]
+
+    for dataset in sorted(retention_table.keys()):
+        row = [dataset]
+        best_ilad = 0
+        winner = ""
+
+        for strategy in STRATEGIES:
+            if strategy in retention_table[dataset]:
+                ilad = retention_table[dataset][strategy]["ilad"]
+                row.append(f"{ilad:.3f}")
+                if ilad > best_ilad:
+                    best_ilad = ilad
+                    winner = strategy.upper()
+            else:
+                row.append("-")
+
+        row.append(f"**{winner}**")
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.append("")
+    return lines
+
+
+def _format_ranking_table(f1_ranking: dict) -> list[str]:
+    """Format the F1 ranking table as markdown lines."""
+    lines = [
+        "## Overall Strategy Ranking\n",
+        "Ranking by average F1 score (harmonic mean of normalized nDCG and ILAD):\n",
+        "| Rank | Strategy | Avg F1 | Interpretation |",
+        "|:----:|----------|:------:|----------------|",
+    ]
+
+    sorted_ranking = sorted(f1_ranking.items(), key=lambda x: -x[1])
+    interpretations = {
+        "msd": "Best diversity with acceptable relevance",
+        "dpp": "Good balance of both metrics",
+        "mmr": "Conservative, relevance-focused",
+        "ssd": "Conservative, relevance-focused",
+    }
+    medals = {0: "🥇", 1: "🥈", 2: "🥉"}
+
+    for i, (strategy, score) in enumerate(sorted_ranking):
+        medal = medals.get(i, f"{i + 1}.")
+        lines.append(f"| {medal} | **{strategy.upper()}** | {score:.3f} | {interpretations.get(strategy, '')} |")
+
+    lines.append("")
+    return lines
+
+
+def _format_f1_details(f1_per_dataset: dict) -> list[str]:
+    """Format the detailed F1 table as markdown lines."""
+    lines = [
+        "## Best F1 Score per Dataset\n",
+        "| Dataset | Strategy | λ | nDCG@10 | ILAD | F1 |",
+        "|---------|----------|--:|--------:|-----:|---:|",
+    ]
+
+    for dataset in sorted(f1_per_dataset.keys()):
+        for strategy in STRATEGIES:
+            if strategy in f1_per_dataset[dataset]:
+                d = f1_per_dataset[dataset][strategy]
+                lines.append(
+                    f"| {dataset} | {strategy.upper()} | {d['lambda']:.1f} | "
+                    f"{d['ndcg']:.4f} | {d['ilad']:.3f} | {d['f1']:.3f} |"
+                )
+
+    lines.append("")
+    return lines
+
+
+def _format_regional_analysis(regional_data: dict) -> list[str]:
+    """Format the regional winner analysis as markdown."""
+    lines = [
+        "## Best Strategy by Diversity Level\n",
+        "Which strategy achieves highest relevance (nDCG) at each diversity (ILAD) level?\n",
+        "| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Maximum (0.9+) |",
+        "|---------|:-------------:|:------------------:|:--------------:|:--------------:|",
+    ]
+
+    region_order = ["Low (0.3-0.5)", "Moderate (0.5-0.7)", "High (0.7-0.9)", "Maximum (0.9+)"]
+
+    for dataset in sorted(regional_data["per_dataset"].keys()):
+        row = [dataset]
+        for region in region_order:
+            if region in regional_data["per_dataset"][dataset]:
+                winner = regional_data["per_dataset"][dataset][region]["winner"].upper()
+                row.append(f"**{winner}**")
+            else:
+                row.append("-")
+        lines.append("| " + " | ".join(row) + " |")
+
+    # Add summary row
+    lines.append("")
+    lines.append("### Summary: Wins per Strategy by Region\n")
+    lines.append("| Region | SSD | DPP | MSD | MMR | Recommendation |")
+    lines.append("|--------|:---:|:---:|:---:|:---:|----------------|")
+
+    recommendations = {
+        "Low (0.3-0.5)": "Use **SSD** for light diversification",
+        "Moderate (0.5-0.7)": "Use **MSD** or **DPP** for moderate diversity",
+        "High (0.7-0.9)": "Use **MSD** for heavy diversification",
+        "Maximum (0.9+)": "Use **MSD** for maximum diversity",
+    }
+
+    for region in region_order:
+        wins = regional_data["aggregate"].get(region, {})
+        row = [region]
+        for s in ["ssd", "dpp", "msd", "mmr"]:
+            count = wins.get(s, 0)
+            row.append(str(count) if count > 0 else "-")
+        row.append(recommendations.get(region, ""))
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.append("")
+    return lines
+
+
+def _format_full_results(results: list[dict]) -> list[str]:
+    """Format full results as collapsible markdown."""
+    lines = [
+        "<details>",
+        "<summary>Full Results by Dataset (click to expand)</summary>\n",
+    ]
+
+    for dataset_result in sorted(results, key=lambda x: x["dataset"]):
+        dataset = dataset_result["dataset"]
+        lines.append(f"### {dataset}\n")
+        lines.append("| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |")
+        lines.append("|----------|--:|--------:|----:|-----:|--------:|")
+
+        for r in sorted(dataset_result["results"], key=lambda x: (x["strategy"], x["diversity"])):
+            lines.append(
+                f"| {r['strategy'].upper()} | {r['diversity']:.1f} | "
+                f"{r['ndcg@10']:.4f} | {r['mrr']:.4f} | {r['ilad']:.3f} | "
+                f"{r['latency_ms']:.2f}ms |"
+            )
+        lines.append("")
+
+    lines.append("</details>\n")
+    return lines
+
+
+def generate_markdown(results: list[dict]) -> str:
+    """Generate markdown report content from benchmark results."""
+    # Compute analysis
+    retention_table = _compute_retention_table(results, retention_pct=0.95)
+    f1_per_dataset, f1_ranking = _compute_f1_scores(results)
+    regional_data = _compute_regional_winners(results)
+
+    md_lines = [
+        "# Pyversity Benchmark Results\n",
+        "Comparison of diversification strategies across recommendation datasets.\n",
+        "## Datasets\n",
+        "| Dataset | Source | Description |",
+        "|---------|--------|-------------|",
+        "| MovieLens-32M | GroupLens | 32M ratings from MovieLens |",
+        "| Last.FM | HetRec 2011 | Music listening data |",
+        "| Amazon Video Games | McAuley Lab | Product reviews |",
+        "| Goodreads | McAuley Lab | Book ratings |",
+        "",
+        "## Strategies\n",
+        "| Strategy | Description | Complexity |",
+        "|----------|-------------|------------|",
+        "| **MMR** | Maximal Marginal Relevance | O(k·n) |",
+        "| **MSD** | Max-Sum Diversification | O(k·n) |",
+        "| **DPP** | Determinantal Point Process | O(k²·n) |",
+        "| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |",
+        "",
+    ]
+
+    # Add analysis tables
+    md_lines.extend(_format_regional_analysis(regional_data))
+    md_lines.extend(_format_retention_table(retention_table))
+    md_lines.extend(_format_ranking_table(f1_ranking))
+    md_lines.extend(_format_f1_details(f1_per_dataset))
+    md_lines.extend(_format_full_results(results))
+
+    return "\n".join(md_lines)
+
+
+def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
+    """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
+    import matplotlib.pyplot as plt
+
+    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+    axes = axes.flatten()
+
+    datasets = sorted(set(d["dataset"] for d in all_data))
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+    colors = {"mmr": "#e74c3c", "msd": "#2ecc71", "dpp": "#3498db", "ssd": "#9b59b6"}
+
+    # Clean dataset names for titles
+    name_map = {
+        "ml-32m": "MovieLens-32M",
+        "lastfm": "Last.FM",
+        "amazon-product-reviews-video-games": "Amazon Video Games",
+        "goodreads-rating": "Goodreads",
+    }
+
+    for ax, dataset in zip(axes, datasets):
+        # Add shaded regions for diversity levels
+        ax.axvspan(0.3, 0.5, alpha=0.1, color="blue", label="_Low")
+        ax.axvspan(0.5, 0.7, alpha=0.1, color="green", label="_Moderate")
+        ax.axvspan(0.7, 0.9, alpha=0.1, color="orange", label="_High")
+        ax.axvspan(0.9, 1.0, alpha=0.1, color="red", label="_Max")
+
+        for strategy in strategies:
+            points = [d for d in all_data if d["dataset"] == dataset and d["strategy"] == strategy]
+            points = sorted(points, key=lambda x: x["lambda"])
+
+            if points:
+                x = [p["ilad"] for p in points]
+                y = [p["ndcg"] for p in points]
+                ax.plot(x, y, "o-", color=colors[strategy], label=strategy.upper(), markersize=7, linewidth=2.5)
+
+        ax.set_xlabel("ILAD (Diversity) →", fontsize=10)
+        ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
+        ax.set_title(name_map.get(dataset, dataset), fontsize=12, fontweight="bold")
+        ax.legend(loc="upper right", fontsize=9, framealpha=0.9)
+        ax.grid(True, alpha=0.3, linestyle="--")
+        ax.set_xlim(0.25, 1.05)
+
+    # Add region labels to first subplot
+    ax0 = axes[0]
+    y_top = ax0.get_ylim()[1]
+    ax0.text(0.4, y_top * 0.95, "Low", ha="center", fontsize=8, color="blue", alpha=0.7)
+    ax0.text(0.6, y_top * 0.95, "Med", ha="center", fontsize=8, color="green", alpha=0.7)
+    ax0.text(0.8, y_top * 0.95, "High", ha="center", fontsize=8, color="orange", alpha=0.7)
+    ax0.text(0.95, y_top * 0.95, "Max", ha="center", fontsize=8, color="red", alpha=0.7)
+
+    plt.suptitle("Relevance vs Diversity Tradeoff by Strategy", fontsize=14, fontweight="bold", y=1.02)
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white")
+    plt.close()
+
+
+def generate_latency_plot(all_data: list[dict], output_path: Path) -> None:
+    """Generate latency comparison bar chart."""
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import seaborn as sns
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+
+    datasets = sorted(set(d["dataset"] for d in all_data))
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+    colors = dict(zip(strategies, sns.color_palette("husl", len(strategies))))
+
+    # Average latency per strategy/dataset at λ=0.6
+    latency_data = {}
+    for d in all_data:
+        if d["lambda"] == 0.6:
+            key = (d["dataset"], d["strategy"])
+            latency_data[key] = d["latency_ms"]
+
+    if not latency_data:
+        plt.close()
+        return
+
+    x_labels: list[str] = []
+    bar_data: dict[str, list[float]] = {s: [] for s in strategies}
+
+    for dataset in datasets:
+        x_labels.append(dataset)
+        for strategy in strategies:
+            val = latency_data.get((dataset, strategy), 0)
+            bar_data[strategy].append(val)
+
+    x = np.arange(len(x_labels))
+    width = 0.2
+
+    for i, (strategy, values) in enumerate(bar_data.items()):
+        ax.bar(x + i * width, values, width, label=strategy.upper(), color=colors[strategy])
+
+    ax.set_ylabel("Latency (ms)")
+    ax.set_title("Latency Comparison at λ=0.6")
+    ax.set_xticks(x + width * 1.5)
+    ax.set_xticklabels(x_labels, rotation=45, ha="right")
+    ax.legend()
+    ax.grid(axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close()
+
+
+def generate_report(results_dir: Path) -> None:
+    """Generate full report from JSON results in a directory."""
+    results = []
+    for path in results_dir.glob("*.json"):
+        with open(path) as f:
+            results.append(json.load(f))
+
+    if not results:
+        logger.warning("No results found.")
+        return
+
+    # Collect all data points
+    all_data = []
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        for r in dataset_result["results"]:
+            all_data.append(
+                {
+                    "dataset": dataset,
+                    "strategy": r["strategy"],
+                    "lambda": r["diversity"],
+                    "ndcg": r["ndcg@10"],
+                    "mrr": r["mrr"],
+                    "ilad": r["ilad"],
+                    "latency_ms": r["latency_ms"],
+                }
+            )
+
+    # Generate markdown
+    md_content = generate_markdown(results)
+    report_path = results_dir / "RESULTS.md"
+    report_path.write_text(md_content)
+    logger.debug(f"Saved: {report_path}")
+
+    # Generate plots
+    pareto_path = results_dir / "pareto.png"
+    generate_pareto_plot(all_data, pareto_path)
+    logger.debug(f"Saved: {pareto_path}")
+
+    latency_path = results_dir / "latency.png"
+    generate_latency_plot(all_data, latency_path)
+    logger.debug(f"Saved: {latency_path}")
+
+    logger.info(f"Report generated: {results_dir}")
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
new file mode 100644
index 0000000..ba515a9
--- /dev/null
+++ b/benchmarks/core/runner.py
@@ -0,0 +1,194 @@
+"""Main benchmark runner."""
+
+from __future__ import annotations
+
+import json
+import logging
+import time
+from datetime import datetime, timezone
+
+import numpy as np
+import pyversity
+from scipy import sparse
+from tqdm import tqdm
+
+from benchmarks.core.config import BenchmarkConfig
+from benchmarks.core.data import InteractionData, load_dataset
+from benchmarks.core.embeddings import compute_similarity_matrix, generate_embeddings, get_candidates
+from benchmarks.core.metrics import ilad, ilmd, mrr, ndcg
+
+logger = logging.getLogger(__name__)
+
+
+def run_benchmark(config: BenchmarkConfig) -> dict:
+    """
+    Run the benchmark suite.
+
+    Args:
+    ----
+        config: Benchmark configuration
+
+    Returns:
+    -------
+        Results dictionary with config and per-strategy metrics
+
+    """
+    if config.dataset is None:
+        raise ValueError("config.dataset must be specified")
+
+    rng = np.random.default_rng(config.seed)
+
+    # Load and prepare data
+    logger.debug("[1/4] Loading dataset...")
+    data = load_dataset(config.dataset, config.min_interactions, config.rating_threshold)
+
+    logger.debug("[2/4] Generating embeddings...")
+    embeddings = generate_embeddings(data, dim=config.embedding_dim, seed=config.seed)
+
+    logger.debug("[3/4] Computing similarity matrix...")
+    similarity = compute_similarity_matrix(embeddings, top_k=100)
+    logger.debug(f"Similarity matrix: {similarity.shape}, nnz={similarity.nnz:,}")
+
+    # Sample users
+    user_counts = np.bincount(data.user_ids, minlength=data.n_users)
+    eligible = np.where(user_counts >= 2)[0]
+    sampled = rng.choice(eligible, min(len(eligible), config.sample_users), replace=False)
+    logger.debug(f"[4/4] Evaluating {len(sampled)} users...")
+
+    # Run evaluation
+    all_results = []
+    for user_id in tqdm(sampled, desc="Users"):
+        user_results = _evaluate_user(user_id, data, embeddings, similarity, config, rng)
+        all_results.extend(user_results)
+
+    # Aggregate results
+    aggregated = _aggregate_results(all_results, config)
+
+    # Build output
+    dataset_name = config.dataset if isinstance(config.dataset, str) else config.dataset.name
+    output = {
+        "dataset": dataset_name,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "config": {
+            "n_users": data.n_users,
+            "n_items": data.n_items,
+            "n_interactions": len(data.user_ids),
+            "sample_users": len(sampled),
+            "k": config.k,
+            "embedding_dim": config.embedding_dim,
+            "seed": config.seed,
+        },
+        "results": aggregated,
+    }
+
+    # Save
+    config.output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = config.output_dir / f"{dataset_name}.json"
+    with open(output_path, "w") as f:
+        json.dump(output, f, indent=2)
+    logger.debug(f"Saved results to: {output_path}")
+
+    # Print summary table (keep as print for nice formatting)
+    _print_summary(aggregated)
+
+    return output
+
+
+def _evaluate_user(
+    user_id: int,
+    data: InteractionData,
+    embeddings: np.ndarray,
+    similarity: sparse.csr_matrix,
+    config: BenchmarkConfig,
+    rng: np.random.Generator,
+) -> list[dict]:
+    """Evaluate all strategies for a single user."""
+    # Get user's items
+    mask = data.user_ids == user_id
+    profile = data.item_ids[mask]
+
+    if len(profile) < 2:
+        return []
+
+    # Leave-one-out split
+    test_idx = rng.integers(len(profile))
+    test_items = np.array([profile[test_idx]])
+    profile_items = np.delete(profile, test_idx)
+
+    # Generate candidates
+    candidate_ids, relevance_scores = get_candidates(
+        profile_items, similarity, config.topk_similar_per_item, config.max_candidates
+    )
+
+    if len(candidate_ids) < config.k:
+        return []
+
+    candidate_embeddings = embeddings[candidate_ids]
+    results = []
+
+    for strategy in config.strategies:
+        for diversity in config.diversity_values:
+            start = time.perf_counter()
+            result = pyversity.diversify(
+                embeddings=candidate_embeddings,
+                scores=relevance_scores,
+                k=config.k,
+                strategy=strategy,
+                diversity=diversity,
+            )
+            latency_ms = (time.perf_counter() - start) * 1000
+
+            selected = candidate_ids[result.indices]
+            results.append(
+                {
+                    "strategy": strategy.value,
+                    "diversity": diversity,
+                    "latency_ms": latency_ms,
+                    "mrr": mrr(selected, test_items),
+                    "ndcg@10": ndcg(selected, test_items, k=10),
+                    "ilad": ilad(selected, embeddings),
+                    "ilmd": ilmd(selected, embeddings),
+                }
+            )
+
+    return results
+
+
+def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dict]:
+    """Aggregate per-user results into summary statistics."""
+    from collections import defaultdict
+
+    groups = defaultdict(list)
+    for r in results:
+        key = (r["strategy"], r["diversity"])
+        groups[key].append(r)
+
+    aggregated = []
+    for (strategy, diversity), group in sorted(groups.items()):
+        agg = {"strategy": strategy, "diversity": diversity}
+        for metric in ["mrr", "ndcg@10", "ilad", "ilmd", "latency_ms"]:
+            values = [r[metric] for r in group]
+            agg[metric] = float(np.mean(values))
+            agg[f"{metric}_std"] = float(np.std(values))
+        aggregated.append(agg)
+
+    return aggregated
+
+
+def _print_summary(results: list[dict]) -> None:
+    """Print summary table (uses print for table formatting)."""
+    print("\n" + "=" * 60)  # noqa: T201
+    print("RESULTS SUMMARY")  # noqa: T201
+    print("=" * 60)  # noqa: T201
+    print(f"{'Strategy':<10} {'λ':>5} {'nDCG@10':>10} {'ILAD':>10} {'Latency':>10}")  # noqa: T201
+    print("-" * 60)  # noqa: T201
+
+    for r in results:
+        print(  # noqa: T201
+            f"{r['strategy']:<10} {r['diversity']:>5.1f} {r['ndcg@10']:>10.4f} {r['ilad']:>10.4f} {r['latency_ms']:>9.2f}ms"
+        )
+
+
+if __name__ == "__main__":
+    # For direct testing - requires dataset to be specified
+    run_benchmark(BenchmarkConfig(dataset="ml-32m"))
diff --git a/benchmarks/results/RESULTS.md b/benchmarks/results/RESULTS.md
new file mode 100644
index 0000000..2db7f0d
--- /dev/null
+++ b/benchmarks/results/RESULTS.md
@@ -0,0 +1,222 @@
+# Pyversity Benchmark Results
+
+Comparison of diversification strategies across recommendation datasets.
+
+## Datasets
+
+| Dataset | Source | Description |
+|---------|--------|-------------|
+| MovieLens-32M | GroupLens | 32M ratings from MovieLens |
+| Last.FM | HetRec 2011 | Music listening data |
+| Amazon Video Games | McAuley Lab | Product reviews |
+| Goodreads | McAuley Lab | Book ratings |
+
+## Strategies
+
+| Strategy | Description | Complexity |
+|----------|-------------|------------|
+| **MMR** | Maximal Marginal Relevance | O(k·n) |
+| **MSD** | Max-Sum Diversification | O(k·n) |
+| **DPP** | Determinantal Point Process | O(k²·n) |
+| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
+
+## Best Strategy by Diversity Level
+
+Which strategy achieves highest relevance (nDCG) at each diversity (ILAD) level?
+
+| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Maximum (0.9+) |
+|---------|:-------------:|:------------------:|:--------------:|:--------------:|
+| amazon-product-reviews-video-games | - | - | **MMR** | **MSD** |
+| goodreads-rating | **SSD** | **MSD** | **MSD** | **MSD** |
+| lastfm | **SSD** | **MSD** | **MSD** | **MSD** |
+| ml-32m | **SSD** | **DPP** | **MSD** | - |
+
+### Summary: Wins per Strategy by Region
+
+| Region | SSD | DPP | MSD | MMR | Recommendation |
+|--------|:---:|:---:|:---:|:---:|----------------|
+| Low (0.3-0.5) | 3 | - | - | - | Use **SSD** for light diversification |
+| Moderate (0.5-0.7) | - | 1 | 2 | - | Use **MSD** or **DPP** for moderate diversity |
+| High (0.7-0.9) | - | - | 3 | 1 | Use **MSD** for heavy diversification |
+| Maximum (0.9+) | - | - | 3 | - | Use **MSD** for maximum diversity |
+
+## Diversity at 95% Relevance Retention
+
+How much diversity (ILAD) can each strategy achieve while maintaining ≥95% of baseline nDCG?
+
+| Dataset | MMR | MSD | DPP | SSD | Winner |
+|---------|:---:|:---:|:---:|:---:|:------:|
+| amazon-product-reviews-video-games | 0.851 | 0.947 | 0.866 | 0.832 | **MSD** |
+| goodreads-rating | 0.427 | 0.724 | 0.673 | 0.462 | **MSD** |
+| lastfm | 0.485 | 0.837 | 0.733 | 0.523 | **MSD** |
+| ml-32m | 0.389 | 0.536 | 0.582 | 0.415 | **DPP** |
+
+## Overall Strategy Ranking
+
+Ranking by average F1 score (harmonic mean of normalized nDCG and ILAD):
+
+| Rank | Strategy | Avg F1 | Interpretation |
+|:----:|----------|:------:|----------------|
+| 🥇 | **MSD** | 0.790 | Best diversity with acceptable relevance |
+| 🥈 | **DPP** | 0.661 | Good balance of both metrics |
+| 🥉 | **MMR** | 0.397 | Conservative, relevance-focused |
+| 4. | **SSD** | 0.334 | Conservative, relevance-focused |
+
+## Best F1 Score per Dataset
+
+| Dataset | Strategy | λ | nDCG@10 | ILAD | F1 |
+|---------|----------|--:|--------:|-----:|---:|
+| amazon-product-reviews-video-games | MMR | 0.8 | 0.2557 | 0.935 | 0.697 |
+| amazon-product-reviews-video-games | MSD | 0.4 | 0.2852 | 0.947 | 0.834 |
+| amazon-product-reviews-video-games | DPP | 0.8 | 0.2982 | 0.866 | 0.593 |
+| amazon-product-reviews-video-games | SSD | 0.8 | 0.2987 | 0.832 | 0.425 |
+| goodreads-rating | MMR | 0.8 | 0.0251 | 0.553 | 0.476 |
+| goodreads-rating | MSD | 0.6 | 0.0244 | 0.832 | 0.779 |
+| goodreads-rating | DPP | 0.8 | 0.0272 | 0.673 | 0.683 |
+| goodreads-rating | SSD | 0.8 | 0.0288 | 0.462 | 0.310 |
+| lastfm | MMR | 0.8 | 0.1590 | 0.485 | 0.195 |
+| lastfm | MSD | 0.6 | 0.1537 | 0.837 | 0.821 |
+| lastfm | DPP | 0.8 | 0.1506 | 0.733 | 0.681 |
+| lastfm | SSD | 0.8 | 0.1585 | 0.523 | 0.298 |
+| ml-32m | MMR | 0.8 | 0.0606 | 0.389 | 0.219 |
+| ml-32m | MSD | 0.6 | 0.0512 | 0.660 | 0.727 |
+| ml-32m | DPP | 0.8 | 0.0616 | 0.582 | 0.688 |
+| ml-32m | SSD | 0.8 | 0.0634 | 0.415 | 0.303 |
+
+<details>
+<summary>Full Results by Dataset (click to expand)</summary>
+
+### amazon-product-reviews-video-games
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
+|----------|--:|--------:|----:|-----:|--------:|
+| DPP | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.03ms |
+| DPP | 0.2 | 0.2904 | 0.2497 | 0.798 | 0.21ms |
+| DPP | 0.4 | 0.2917 | 0.2506 | 0.806 | 0.20ms |
+| DPP | 0.6 | 0.2937 | 0.2532 | 0.824 | 0.20ms |
+| DPP | 0.8 | 0.2982 | 0.2576 | 0.866 | 0.21ms |
+| DPP | 1.0 | 0.1711 | 0.1696 | 0.973 | 0.20ms |
+| MMR | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.20ms |
+| MMR | 0.2 | 0.2893 | 0.2477 | 0.785 | 0.17ms |
+| MMR | 0.4 | 0.2961 | 0.2546 | 0.806 | 0.16ms |
+| MMR | 0.6 | 0.3037 | 0.2592 | 0.851 | 0.15ms |
+| MMR | 0.8 | 0.2557 | 0.2287 | 0.935 | 0.16ms |
+| MMR | 1.0 | 0.1727 | 0.1722 | 0.972 | 0.16ms |
+| MSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.17ms |
+| MSD | 0.2 | 0.2916 | 0.2467 | 0.881 | 0.17ms |
+| MSD | 0.4 | 0.2852 | 0.2428 | 0.947 | 0.16ms |
+| MSD | 0.6 | 0.2511 | 0.2258 | 0.975 | 0.16ms |
+| MSD | 0.8 | 0.1946 | 0.1864 | 0.986 | 0.17ms |
+| MSD | 1.0 | 0.1648 | 0.1644 | 0.990 | 0.16ms |
+| SSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.01ms |
+| SSD | 0.2 | 0.2814 | 0.2423 | 0.776 | 1.88ms |
+| SSD | 0.4 | 0.2845 | 0.2451 | 0.782 | 1.80ms |
+| SSD | 0.6 | 0.2911 | 0.2501 | 0.793 | 1.72ms |
+| SSD | 0.8 | 0.2987 | 0.2575 | 0.832 | 1.64ms |
+| SSD | 1.0 | 0.1644 | 0.1653 | 0.980 | 1.66ms |
+
+### goodreads-rating
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
+|----------|--:|--------:|----:|-----:|--------:|
+| DPP | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.05ms |
+| DPP | 0.2 | 0.0290 | 0.0236 | 0.414 | 0.39ms |
+| DPP | 0.4 | 0.0286 | 0.0236 | 0.438 | 0.40ms |
+| DPP | 0.6 | 0.0284 | 0.0234 | 0.490 | 0.35ms |
+| DPP | 0.8 | 0.0272 | 0.0221 | 0.673 | 0.35ms |
+| DPP | 1.0 | 0.0111 | 0.0114 | 0.879 | 0.35ms |
+| MMR | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.28ms |
+| MMR | 0.2 | 0.0284 | 0.0236 | 0.367 | 0.24ms |
+| MMR | 0.4 | 0.0288 | 0.0236 | 0.388 | 0.23ms |
+| MMR | 0.6 | 0.0275 | 0.0229 | 0.427 | 0.24ms |
+| MMR | 0.8 | 0.0251 | 0.0211 | 0.553 | 0.23ms |
+| MMR | 1.0 | 0.0127 | 0.0125 | 0.863 | 0.23ms |
+| MSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.24ms |
+| MSD | 0.2 | 0.0288 | 0.0234 | 0.572 | 0.24ms |
+| MSD | 0.4 | 0.0277 | 0.0219 | 0.724 | 0.24ms |
+| MSD | 0.6 | 0.0244 | 0.0198 | 0.832 | 0.24ms |
+| MSD | 0.8 | 0.0182 | 0.0159 | 0.898 | 0.24ms |
+| MSD | 1.0 | 0.0115 | 0.0113 | 0.927 | 0.24ms |
+| SSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.01ms |
+| SSD | 0.2 | 0.0284 | 0.0238 | 0.360 | 3.68ms |
+| SSD | 0.4 | 0.0284 | 0.0237 | 0.369 | 3.61ms |
+| SSD | 0.6 | 0.0293 | 0.0236 | 0.387 | 3.53ms |
+| SSD | 0.8 | 0.0288 | 0.0236 | 0.462 | 3.53ms |
+| SSD | 1.0 | 0.0115 | 0.0116 | 0.888 | 3.55ms |
+
+### lastfm
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
+|----------|--:|--------:|----:|-----:|--------:|
+| DPP | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.05ms |
+| DPP | 0.2 | 0.1592 | 0.1326 | 0.471 | 0.40ms |
+| DPP | 0.4 | 0.1593 | 0.1329 | 0.490 | 0.41ms |
+| DPP | 0.6 | 0.1568 | 0.1320 | 0.542 | 0.36ms |
+| DPP | 0.8 | 0.1506 | 0.1258 | 0.733 | 0.35ms |
+| DPP | 1.0 | 0.0784 | 0.0782 | 0.947 | 0.35ms |
+| MMR | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.28ms |
+| MMR | 0.2 | 0.1591 | 0.1319 | 0.429 | 0.24ms |
+| MMR | 0.4 | 0.1597 | 0.1326 | 0.433 | 0.23ms |
+| MMR | 0.6 | 0.1605 | 0.1334 | 0.443 | 0.23ms |
+| MMR | 0.8 | 0.1590 | 0.1332 | 0.485 | 0.23ms |
+| MMR | 1.0 | 0.0780 | 0.0781 | 0.942 | 0.24ms |
+| MSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.25ms |
+| MSD | 0.2 | 0.1606 | 0.1330 | 0.506 | 0.24ms |
+| MSD | 0.4 | 0.1583 | 0.1317 | 0.638 | 0.24ms |
+| MSD | 0.6 | 0.1537 | 0.1261 | 0.837 | 0.24ms |
+| MSD | 0.8 | 0.1270 | 0.1118 | 0.940 | 0.25ms |
+| MSD | 1.0 | 0.0779 | 0.0777 | 0.977 | 0.24ms |
+| SSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.02ms |
+| SSD | 0.2 | 0.1590 | 0.1324 | 0.429 | 3.25ms |
+| SSD | 0.4 | 0.1600 | 0.1329 | 0.435 | 3.20ms |
+| SSD | 0.6 | 0.1608 | 0.1336 | 0.449 | 3.12ms |
+| SSD | 0.8 | 0.1585 | 0.1328 | 0.523 | 3.12ms |
+| SSD | 1.0 | 0.0775 | 0.0776 | 0.958 | 3.09ms |
+
+### ml-32m
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
+|----------|--:|--------:|----:|-----:|--------:|
+| DPP | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.04ms |
+| DPP | 0.2 | 0.0614 | 0.0465 | 0.394 | 0.42ms |
+| DPP | 0.4 | 0.0620 | 0.0471 | 0.418 | 0.37ms |
+| DPP | 0.6 | 0.0629 | 0.0477 | 0.473 | 0.33ms |
+| DPP | 0.8 | 0.0616 | 0.0462 | 0.582 | 0.32ms |
+| DPP | 1.0 | 0.0151 | 0.0167 | 0.734 | 0.35ms |
+| MMR | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.27ms |
+| MMR | 0.2 | 0.0553 | 0.0427 | 0.333 | 0.26ms |
+| MMR | 0.4 | 0.0566 | 0.0435 | 0.338 | 0.25ms |
+| MMR | 0.6 | 0.0575 | 0.0445 | 0.348 | 0.30ms |
+| MMR | 0.8 | 0.0606 | 0.0445 | 0.389 | 0.23ms |
+| MMR | 1.0 | 0.0156 | 0.0162 | 0.721 | 0.26ms |
+| MSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.23ms |
+| MSD | 0.2 | 0.0567 | 0.0434 | 0.416 | 0.23ms |
+| MSD | 0.4 | 0.0575 | 0.0426 | 0.536 | 0.24ms |
+| MSD | 0.6 | 0.0512 | 0.0392 | 0.660 | 0.24ms |
+| MSD | 0.8 | 0.0411 | 0.0324 | 0.752 | 0.24ms |
+| MSD | 1.0 | 0.0132 | 0.0132 | 0.801 | 0.23ms |
+| SSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.01ms |
+| SSD | 0.2 | 0.0565 | 0.0431 | 0.335 | 3.45ms |
+| SSD | 0.4 | 0.0579 | 0.0445 | 0.341 | 3.38ms |
+| SSD | 0.6 | 0.0612 | 0.0456 | 0.354 | 3.25ms |
+| SSD | 0.8 | 0.0634 | 0.0472 | 0.415 | 3.25ms |
+| SSD | 1.0 | 0.0149 | 0.0166 | 0.736 | 3.21ms |
+
+</details>
+
+## Key Insights
+
+1. **SSD excels at light diversification** - Best nDCG when ILAD is 0.3-0.5
+2. **MSD dominates high diversity regions** - Best choice when ILAD > 0.7
+3. **DPP is a good middle-ground** - Competitive across multiple regions
+4. **MMR is fastest but least effective** - Rarely wins any diversity region
+
+### Choosing the Right Strategy
+
+| Your Goal | Best Strategy | λ | Why |
+|-----------|---------------|---|-----|
+| Light diversification | **SSD** | 0.6-0.8 | Highest nDCG at ILAD 0.3-0.5 |
+| Moderate diversification | **DPP** or **MSD** | 0.4-0.6 | Good balance at ILAD 0.5-0.7 |
+| Heavy diversification | **MSD** | 0.4-0.6 | Best at ILAD 0.7-0.9 |
+| Maximum diversity | **MSD** | 0.6-0.8 | Dominates at ILAD > 0.9 |
+| Speed-critical | **MMR** | 0.6-0.8 | O(k·n) complexity |
diff --git a/benchmarks/results/amazon-product-reviews-video-games.json b/benchmarks/results/amazon-product-reviews-video-games.json
new file mode 100644
index 0000000..cfe8bab
--- /dev/null
+++ b/benchmarks/results/amazon-product-reviews-video-games.json
@@ -0,0 +1,351 @@
+{
+  "dataset": "amazon-product-reviews-video-games",
+  "timestamp": "2026-01-22T14:27:20.887176+00:00",
+  "config": {
+    "n_users": 707,
+    "n_items": 537,
+    "n_interactions": 5834,
+    "sample_users": 707,
+    "k": 20,
+    "embedding_dim": 64,
+    "seed": 42
+  },
+  "results": [
+    {
+      "strategy": "dpp",
+      "diversity": 0.0,
+      "mrr": 0.24030748494798834,
+      "mrr_std": 0.35792092111973967,
+      "ndcg@10": 0.2790212189683253,
+      "ndcg@10_std": 0.3720897481865557,
+      "ilad": 0.7735240327502578,
+      "ilad_std": 0.08175771190159127,
+      "ilmd": 0.18095086495980853,
+      "ilmd_std": 0.07534733499987091,
+      "latency_ms": 0.028420680072790874,
+      "latency_ms_std": 0.03997715083763415
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.2,
+      "mrr": 0.249695691096587,
+      "mrr_std": 0.3576023080438181,
+      "ndcg@10": 0.2904184030580189,
+      "ndcg@10_std": 0.3727431536021708,
+      "ilad": 0.7978060674633636,
+      "ilad_std": 0.07251291136105421,
+      "ilmd": 0.22800128522317176,
+      "ilmd_std": 0.08378991507768188,
+      "latency_ms": 0.20679205224969158,
+      "latency_ms_std": 0.10557277379005317
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.4,
+      "mrr": 0.250642548173486,
+      "mrr_std": 0.35734181587645153,
+      "ndcg@10": 0.29174199036871673,
+      "ndcg@10_std": 0.3725686105704199,
+      "ilad": 0.806259483731282,
+      "ilad_std": 0.06944610368057494,
+      "ilmd": 0.24522726387417806,
+      "ilmd_std": 0.0877306977132351,
+      "latency_ms": 0.2032714310369574,
+      "latency_ms_std": 0.1481942113004484
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.6,
+      "mrr": 0.25319042883578863,
+      "mrr_std": 0.35707058390923413,
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diff --git a/benchmarks/results/goodreads-rating.json b/benchmarks/results/goodreads-rating.json
new file mode 100644
index 0000000..d0c40c6
--- /dev/null
+++ b/benchmarks/results/goodreads-rating.json
@@ -0,0 +1,351 @@
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diff --git a/benchmarks/results/lastfm.json b/benchmarks/results/lastfm.json
new file mode 100644
index 0000000..e0b62f7
--- /dev/null
+++ b/benchmarks/results/lastfm.json
@@ -0,0 +1,351 @@
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+      "ilad_std": 0.034173439605443064,
+      "ilmd": 0.6540523916883709,
+      "ilmd_std": 0.08863750924339966,
+      "latency_ms": 3.0886969103759947,
+      "latency_ms_std": 1.5319832074444872
+    }
+  ]
+}
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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diff --git a/benchmarks/results/ml-32m.json b/benchmarks/results/ml-32m.json
new file mode 100644
index 0000000..4088ba2
--- /dev/null
+++ b/benchmarks/results/ml-32m.json
@@ -0,0 +1,351 @@
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diff --git a/pyproject.toml b/pyproject.toml
index dbe5b00..c8e1882 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,16 @@ dev = [
     "pytest-coverage",
     "ruff",
 ]
+benchmarks = [
+    "datasets",
+    "matplotlib",
+    "pandas",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "seaborn",
+    "tqdm",
+]
 
 [tool.ruff]
 exclude = [".venv/"]
@@ -102,3 +112,10 @@ pyversity = ["py.typed"]
 
 [tool.setuptools.dynamic]
 version = {attr = "pyversity.version.__version__"}
+
+[dependency-groups]
+dev = [
+    "datasets>=4.5.0",
+    "huggingface-hub>=1.3.2",
+    "tqdm>=4.67.1",
+]
diff --git a/src/pyversity/strategies/dpp.py b/src/pyversity/strategies/dpp.py
index 0905af3..e59ccf6 100644
--- a/src/pyversity/strategies/dpp.py
+++ b/src/pyversity/strategies/dpp.py
@@ -57,6 +57,19 @@ def dpp(
     # Normalize feature vectors to unit length for cosine similarity
     feature_matrix = normalize_rows(feature_matrix)
 
+    # Fast path for pure relevance (diversity = 0)
+    if float(diversity) == 0.0:
+        # Pure relevance: select top-k by relevance scores
+        topk = np.argsort(-relevance_scores)[:top_k].astype(np.int32)
+        gains = relevance_scores[topk].astype(np.float32, copy=False)
+        return DiversificationResult(
+            indices=topk,
+            selection_scores=gains,
+            strategy=Strategy.DPP,
+            diversity=diversity,
+            parameters={"scale": scale},
+        )
+
     num_items = feature_matrix.shape[0]
     weights = _exp_zscore_weights(relevance_scores, beta)
 
diff --git a/uv.lock b/uv.lock
index 478bac6..4242bc4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,10 +2,167 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.11'",
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
     "python_full_version < '3.11'",
 ]
 
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+name = "aiohappyeyeballs"
+version = "2.6.1"
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+wheels = [
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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From f369c031438dab4d5be650ec1da9fbb1c8ebde78 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 22 Jan 2026 19:00:37 +0100
Subject: [PATCH 02/69] Improved benchmark code

---
 benchmarks/core/config.py     |   9 +-
 benchmarks/core/data.py       |   2 +-
 benchmarks/core/embeddings.py |   2 +-
 benchmarks/core/metrics.py    |  10 +-
 benchmarks/core/report.py     | 177 ++++++++++++++++------------------
 benchmarks/core/runner.py     |  11 ++-
 6 files changed, 103 insertions(+), 108 deletions(-)

diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index 98699f5..4410827 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -1,4 +1,4 @@
-"""Benchmark configuration."""
+"""Benchmark configuration dataclass with defaults for all parameters."""
 
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -8,7 +8,12 @@
 
 @dataclass
 class BenchmarkConfig:
-    """Configuration for benchmark runs."""
+    """
+    Configuration for benchmark runs.
+
+    All parameters have sensible defaults. Only `dataset` must be specified
+    before running a benchmark.
+    """
 
     # Dataset (name from registry or DatasetInfo)
     dataset: str | None = None
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
index a486f01..1b0a0e1 100644
--- a/benchmarks/core/data.py
+++ b/benchmarks/core/data.py
@@ -1,4 +1,4 @@
-"""Data loading and preprocessing."""
+"""Dataset loading, preprocessing, and registry for benchmark evaluation."""
 
 from __future__ import annotations
 
diff --git a/benchmarks/core/embeddings.py b/benchmarks/core/embeddings.py
index da094c6..f98196e 100644
--- a/benchmarks/core/embeddings.py
+++ b/benchmarks/core/embeddings.py
@@ -1,4 +1,4 @@
-"""Embedding generation and similarity computation."""
+"""Item embedding generation via SVD and similarity matrix computation."""
 
 import logging
 
diff --git a/benchmarks/core/metrics.py b/benchmarks/core/metrics.py
index 2630a37..abfb825 100644
--- a/benchmarks/core/metrics.py
+++ b/benchmarks/core/metrics.py
@@ -1,11 +1,11 @@
-"""Evaluation metrics."""
+"""Relevance and diversity metrics for recommendation evaluation."""
 
 import numpy as np
 from numpy.typing import NDArray
 
 
 def mrr(recommendations: NDArray[np.int64], relevant: NDArray[np.int64]) -> float:
-    """Mean Reciprocal Rank."""
+    """Compute Mean Reciprocal Rank."""
     relevant_set = set(relevant)
     for rank, item in enumerate(recommendations, start=1):
         if item in relevant_set:
@@ -14,7 +14,7 @@ def mrr(recommendations: NDArray[np.int64], relevant: NDArray[np.int64]) -> floa
 
 
 def ndcg(recommendations: NDArray[np.int64], relevant: NDArray[np.int64], k: int = 10) -> float:
-    """Normalized Discounted Cumulative Gain at k."""
+    """Compute Normalized Discounted Cumulative Gain at k."""
     relevant_set = set(relevant)
     recommendations = recommendations[:k]
 
@@ -25,7 +25,7 @@ def ndcg(recommendations: NDArray[np.int64], relevant: NDArray[np.int64], k: int
 
 
 def ilad(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
-    """Intra-List Average Distance (diversity)."""
+    """Compute Intra-List Average Distance (average pairwise dissimilarity)."""
     if len(item_ids) < 2:
         return 0.0
 
@@ -38,7 +38,7 @@ def ilad(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
 
 
 def ilmd(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
-    """Intra-List Minimum Distance (worst-case diversity)."""
+    """Compute Intra-List Minimum Distance (minimum pairwise dissimilarity)."""
     if len(item_ids) < 2:
         return 0.0
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 8dfcb6a..050d72b 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -1,4 +1,4 @@
-"""Report generation for benchmark results."""
+"""Markdown report and plot generation from benchmark results."""
 
 from __future__ import annotations
 
@@ -13,24 +13,24 @@
 
 def _compute_retention_table(results: list[dict], retention_pct: float = 0.95) -> dict:
     """Compute diversity achieved at given relevance retention threshold."""
-    table: dict[str, dict] = {}
+    table: dict[str, dict[str, dict[str, float]]] = {}
 
     for dataset_result in results:
         dataset = dataset_result["dataset"]
         table[dataset] = {}
 
         for strategy in STRATEGIES:
-            runs = [r for r in dataset_result["results"] if r["strategy"] == strategy]
-            baseline = next((r for r in runs if r["diversity"] == 0.0), None)
+            runs = [run for run in dataset_result["results"] if run["strategy"] == strategy]
+            baseline = next((run for run in runs if run["diversity"] == 0.0), None)
 
             if not baseline:
                 continue
 
             threshold = baseline["ndcg@10"] * retention_pct
-            valid = [r for r in runs if r["ndcg@10"] >= threshold and r["diversity"] > 0]
+            valid = [run for run in runs if run["ndcg@10"] >= threshold and run["diversity"] > 0]
 
             if valid:
-                best = max(valid, key=lambda r: r["ilad"])
+                best = max(valid, key=lambda run: run["ilad"])
                 table[dataset][strategy] = {
                     "ilad": best["ilad"],
                     "lambda": best["diversity"],
@@ -42,8 +42,7 @@ def _compute_retention_table(results: list[dict], retention_pct: float = 0.95) -
 
 
 def _compute_regional_winners(results: list[dict]) -> dict:
-    """Compute which strategy wins in each diversity region."""
-    # Define diversity regions
+    """Compute which strategy achieves best nDCG in each diversity region."""
     regions = {
         "Low (0.3-0.5)": (0.3, 0.5),
         "Moderate (0.5-0.7)": (0.5, 0.7),
@@ -51,30 +50,28 @@ def _compute_regional_winners(results: list[dict]) -> dict:
         "Maximum (0.9+)": (0.9, 1.0),
     }
 
-    # Collect all points
     all_points = []
     for dataset_result in results:
         dataset = dataset_result["dataset"]
-        for r in dataset_result["results"]:
+        for run in dataset_result["results"]:
             all_points.append(
                 {
                     "dataset": dataset,
-                    "strategy": r["strategy"],
-                    "ndcg": r["ndcg@10"],
-                    "ilad": r["ilad"],
+                    "strategy": run["strategy"],
+                    "ndcg": run["ndcg@10"],
+                    "ilad": run["ilad"],
                 }
             )
 
-    # Find winner for each dataset/region
-    regional_data: dict[str, dict] = {}
-    datasets = sorted(set(p["dataset"] for p in all_points))
+    regional_data: dict[str, dict[str, dict[str, str | float]]] = {}
+    datasets = sorted(set(point["dataset"] for point in all_points))
 
     for dataset in datasets:
-        ds_points = [p for p in all_points if p["dataset"] == dataset]
+        dataset_points = [point for point in all_points if point["dataset"] == dataset]
         regional_data[dataset] = {}
 
         for region_name, (lo, hi) in regions.items():
-            in_region = [p for p in ds_points if lo <= p["ilad"] < hi]
+            in_region = [point for point in dataset_points if lo <= point["ilad"] < hi]
             if in_region:
                 best = max(in_region, key=lambda x: x["ndcg"])
                 regional_data[dataset][region_name] = {
@@ -83,11 +80,10 @@ def _compute_regional_winners(results: list[dict]) -> dict:
                     "ilad": best["ilad"],
                 }
 
-    # Aggregate wins per region
-    region_wins: dict[str, dict[str, int]] = {r: {} for r in regions}
+    region_wins: dict[str, dict[str, int]] = {region: {} for region in regions}
     for dataset, regions_dict in regional_data.items():
         for region_name, data in regions_dict.items():
-            winner = data["winner"]
+            winner = str(data["winner"])
             region_wins[region_name][winner] = region_wins[region_name].get(winner, 0) + 1
 
     return {"per_dataset": regional_data, "aggregate": region_wins}
@@ -95,37 +91,38 @@ def _compute_regional_winners(results: list[dict]) -> dict:
 
 def _compute_f1_scores(results: list[dict]) -> tuple[dict, dict]:
     """Compute normalized F1 scores (harmonic mean of nDCG and ILAD)."""
-    best_per_dataset: dict[str, dict] = {}
-    all_scores: list[dict] = []
+    best_per_dataset: dict[str, dict[str, dict[str, float]]] = {}
+    all_scores: list[dict[str, str | float]] = []
 
     for dataset_result in results:
         dataset = dataset_result["dataset"]
         best_per_dataset[dataset] = {}
 
-        # Get min/max for normalization within dataset
-        ndcgs = [r["ndcg@10"] for r in dataset_result["results"]]
-        ilads = [r["ilad"] for r in dataset_result["results"]]
+        ndcgs = [run["ndcg@10"] for run in dataset_result["results"]]
+        ilads = [run["ilad"] for run in dataset_result["results"]]
         ndcg_min, ndcg_max = min(ndcgs), max(ndcgs)
         ilad_min, ilad_max = min(ilads), max(ilads)
 
         for strategy in STRATEGIES:
-            runs = [r for r in dataset_result["results"] if r["strategy"] == strategy and 0 < r["diversity"] < 1]
+            runs = [
+                run for run in dataset_result["results"] if run["strategy"] == strategy and 0 < run["diversity"] < 1
+            ]
 
-            best_f1: float = 0.0
+            best_f1 = 0.0
             best_run = None
 
-            for r in runs:
-                ndcg_norm = (r["ndcg@10"] - ndcg_min) / (ndcg_max - ndcg_min) if ndcg_max > ndcg_min else 0
-                ilad_norm = (r["ilad"] - ilad_min) / (ilad_max - ilad_min) if ilad_max > ilad_min else 0
+            for run in runs:
+                ndcg_norm = (run["ndcg@10"] - ndcg_min) / (ndcg_max - ndcg_min) if ndcg_max > ndcg_min else 0.0
+                ilad_norm = (run["ilad"] - ilad_min) / (ilad_max - ilad_min) if ilad_max > ilad_min else 0.0
 
                 if ndcg_norm + ilad_norm > 0:
                     f1 = 2 * ndcg_norm * ilad_norm / (ndcg_norm + ilad_norm)
                 else:
-                    f1 = 0
+                    f1 = 0.0
 
                 if f1 > best_f1:
                     best_f1 = f1
-                    best_run = r
+                    best_run = run
 
             if best_run:
                 best_per_dataset[dataset][strategy] = {
@@ -136,10 +133,9 @@ def _compute_f1_scores(results: list[dict]) -> tuple[dict, dict]:
                 }
                 all_scores.append({"strategy": strategy, "f1": best_f1})
 
-    # Compute average F1 per strategy
     strategy_avg: dict[str, float] = {}
     for strategy in STRATEGIES:
-        scores: list[float] = [s["f1"] for s in all_scores if s["strategy"] == strategy]
+        scores = [float(entry["f1"]) for entry in all_scores if entry["strategy"] == strategy]
         strategy_avg[strategy] = sum(scores) / len(scores) if scores else 0.0
 
     return best_per_dataset, strategy_avg
@@ -192,11 +188,11 @@ def _format_ranking_table(f1_ranking: dict) -> list[str]:
         "mmr": "Conservative, relevance-focused",
         "ssd": "Conservative, relevance-focused",
     }
-    medals = {0: "🥇", 1: "🥈", 2: "🥉"}
+    rank_labels = {0: "1.", 1: "2.", 2: "3."}
 
-    for i, (strategy, score) in enumerate(sorted_ranking):
-        medal = medals.get(i, f"{i + 1}.")
-        lines.append(f"| {medal} | **{strategy.upper()}** | {score:.3f} | {interpretations.get(strategy, '')} |")
+    for idx, (strategy, score) in enumerate(sorted_ranking):
+        rank = rank_labels.get(idx, f"{idx + 1}.")
+        lines.append(f"| {rank} | **{strategy.upper()}** | {score:.3f} | {interpretations.get(strategy, '')} |")
 
     lines.append("")
     return lines
@@ -213,10 +209,10 @@ def _format_f1_details(f1_per_dataset: dict) -> list[str]:
     for dataset in sorted(f1_per_dataset.keys()):
         for strategy in STRATEGIES:
             if strategy in f1_per_dataset[dataset]:
-                d = f1_per_dataset[dataset][strategy]
+                entry = f1_per_dataset[dataset][strategy]
                 lines.append(
-                    f"| {dataset} | {strategy.upper()} | {d['lambda']:.1f} | "
-                    f"{d['ndcg']:.4f} | {d['ilad']:.3f} | {d['f1']:.3f} |"
+                    f"| {dataset} | {strategy.upper()} | {entry['lambda']:.1f} | "
+                    f"{entry['ndcg']:.4f} | {entry['ilad']:.3f} | {entry['f1']:.3f} |"
                 )
 
     lines.append("")
@@ -283,11 +279,11 @@ def _format_full_results(results: list[dict]) -> list[str]:
         lines.append("| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |")
         lines.append("|----------|--:|--------:|----:|-----:|--------:|")
 
-        for r in sorted(dataset_result["results"], key=lambda x: (x["strategy"], x["diversity"])):
+        for run in sorted(dataset_result["results"], key=lambda x: (x["strategy"], x["diversity"])):
             lines.append(
-                f"| {r['strategy'].upper()} | {r['diversity']:.1f} | "
-                f"{r['ndcg@10']:.4f} | {r['mrr']:.4f} | {r['ilad']:.3f} | "
-                f"{r['latency_ms']:.2f}ms |"
+                f"| {run['strategy'].upper()} | {run['diversity']:.1f} | "
+                f"{run['ndcg@10']:.4f} | {run['mrr']:.4f} | {run['ilad']:.3f} | "
+                f"{run['latency_ms']:.2f}ms |"
             )
         lines.append("")
 
@@ -297,7 +293,6 @@ def _format_full_results(results: list[dict]) -> list[str]:
 
 def generate_markdown(results: list[dict]) -> str:
     """Generate markdown report content from benchmark results."""
-    # Compute analysis
     retention_table = _compute_retention_table(results, retention_pct=0.95)
     f1_per_dataset, f1_ranking = _compute_f1_scores(results)
     regional_data = _compute_regional_winners(results)
@@ -323,7 +318,6 @@ def generate_markdown(results: list[dict]) -> str:
         "",
     ]
 
-    # Add analysis tables
     md_lines.extend(_format_regional_analysis(regional_data))
     md_lines.extend(_format_retention_table(retention_table))
     md_lines.extend(_format_ranking_table(f1_ranking))
@@ -340,11 +334,10 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
     axes = axes.flatten()
 
-    datasets = sorted(set(d["dataset"] for d in all_data))
+    datasets = sorted(set(point["dataset"] for point in all_data))
     strategies = ["mmr", "msd", "dpp", "ssd"]
     colors = {"mmr": "#e74c3c", "msd": "#2ecc71", "dpp": "#3498db", "ssd": "#9b59b6"}
 
-    # Clean dataset names for titles
     name_map = {
         "ml-32m": "MovieLens-32M",
         "lastfm": "Last.FM",
@@ -353,20 +346,21 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
     }
 
     for ax, dataset in zip(axes, datasets):
-        # Add shaded regions for diversity levels
         ax.axvspan(0.3, 0.5, alpha=0.1, color="blue", label="_Low")
         ax.axvspan(0.5, 0.7, alpha=0.1, color="green", label="_Moderate")
         ax.axvspan(0.7, 0.9, alpha=0.1, color="orange", label="_High")
         ax.axvspan(0.9, 1.0, alpha=0.1, color="red", label="_Max")
 
         for strategy in strategies:
-            points = [d for d in all_data if d["dataset"] == dataset and d["strategy"] == strategy]
-            points = sorted(points, key=lambda x: x["lambda"])
-
-            if points:
-                x = [p["ilad"] for p in points]
-                y = [p["ndcg"] for p in points]
-                ax.plot(x, y, "o-", color=colors[strategy], label=strategy.upper(), markersize=7, linewidth=2.5)
+            strategy_points = [p for p in all_data if p["dataset"] == dataset and p["strategy"] == strategy]
+            strategy_points = sorted(strategy_points, key=lambda x: x["lambda"])
+
+            if strategy_points:
+                x_vals = [p["ilad"] for p in strategy_points]
+                y_vals = [p["ndcg"] for p in strategy_points]
+                ax.plot(
+                    x_vals, y_vals, "o-", color=colors[strategy], label=strategy.upper(), markersize=7, linewidth=2.5
+                )
 
         ax.set_xlabel("ILAD (Diversity) →", fontsize=10)
         ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
@@ -375,13 +369,12 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
         ax.grid(True, alpha=0.3, linestyle="--")
         ax.set_xlim(0.25, 1.05)
 
-    # Add region labels to first subplot
-    ax0 = axes[0]
-    y_top = ax0.get_ylim()[1]
-    ax0.text(0.4, y_top * 0.95, "Low", ha="center", fontsize=8, color="blue", alpha=0.7)
-    ax0.text(0.6, y_top * 0.95, "Med", ha="center", fontsize=8, color="green", alpha=0.7)
-    ax0.text(0.8, y_top * 0.95, "High", ha="center", fontsize=8, color="orange", alpha=0.7)
-    ax0.text(0.95, y_top * 0.95, "Max", ha="center", fontsize=8, color="red", alpha=0.7)
+    first_ax = axes[0]
+    y_top = first_ax.get_ylim()[1]
+    first_ax.text(0.4, y_top * 0.95, "Low", ha="center", fontsize=8, color="blue", alpha=0.7)
+    first_ax.text(0.6, y_top * 0.95, "Med", ha="center", fontsize=8, color="green", alpha=0.7)
+    first_ax.text(0.8, y_top * 0.95, "High", ha="center", fontsize=8, color="orange", alpha=0.7)
+    first_ax.text(0.95, y_top * 0.95, "Max", ha="center", fontsize=8, color="red", alpha=0.7)
 
     plt.suptitle("Relevance vs Diversity Tradeoff by Strategy", fontsize=14, fontweight="bold", y=1.02)
     plt.tight_layout()
@@ -397,39 +390,38 @@ def generate_latency_plot(all_data: list[dict], output_path: Path) -> None:
 
     fig, ax = plt.subplots(figsize=(10, 5))
 
-    datasets = sorted(set(d["dataset"] for d in all_data))
+    datasets = sorted(set(point["dataset"] for point in all_data))
     strategies = ["mmr", "msd", "dpp", "ssd"]
     colors = dict(zip(strategies, sns.color_palette("husl", len(strategies))))
 
-    # Average latency per strategy/dataset at λ=0.6
-    latency_data = {}
-    for d in all_data:
-        if d["lambda"] == 0.6:
-            key = (d["dataset"], d["strategy"])
-            latency_data[key] = d["latency_ms"]
+    latency_by_key: dict[tuple[str, str], float] = {}
+    for point in all_data:
+        if point["lambda"] == 0.6:
+            key = (point["dataset"], point["strategy"])
+            latency_by_key[key] = point["latency_ms"]
 
-    if not latency_data:
+    if not latency_by_key:
         plt.close()
         return
 
     x_labels: list[str] = []
-    bar_data: dict[str, list[float]] = {s: [] for s in strategies}
+    bar_data: dict[str, list[float]] = {strategy: [] for strategy in strategies}
 
     for dataset in datasets:
         x_labels.append(dataset)
         for strategy in strategies:
-            val = latency_data.get((dataset, strategy), 0)
-            bar_data[strategy].append(val)
+            latency = latency_by_key.get((dataset, strategy), 0.0)
+            bar_data[strategy].append(latency)
 
-    x = np.arange(len(x_labels))
-    width = 0.2
+    x_positions = np.arange(len(x_labels))
+    bar_width = 0.2
 
-    for i, (strategy, values) in enumerate(bar_data.items()):
-        ax.bar(x + i * width, values, width, label=strategy.upper(), color=colors[strategy])
+    for idx, (strategy, values) in enumerate(bar_data.items()):
+        ax.bar(x_positions + idx * bar_width, values, bar_width, label=strategy.upper(), color=colors[strategy])
 
     ax.set_ylabel("Latency (ms)")
     ax.set_title("Latency Comparison at λ=0.6")
-    ax.set_xticks(x + width * 1.5)
+    ax.set_xticks(x_positions + bar_width * 1.5)
     ax.set_xticklabels(x_labels, rotation=45, ha="right")
     ax.legend()
     ax.grid(axis="y", alpha=0.3)
@@ -440,40 +432,37 @@ def generate_latency_plot(all_data: list[dict], output_path: Path) -> None:
 
 
 def generate_report(results_dir: Path) -> None:
-    """Generate full report from JSON results in a directory."""
+    """Generate markdown report and plots from JSON results."""
     results = []
-    for path in results_dir.glob("*.json"):
-        with open(path) as f:
+    for json_path in results_dir.glob("*.json"):
+        with open(json_path) as f:
             results.append(json.load(f))
 
     if not results:
         logger.warning("No results found.")
         return
 
-    # Collect all data points
     all_data = []
     for dataset_result in results:
         dataset = dataset_result["dataset"]
-        for r in dataset_result["results"]:
+        for run in dataset_result["results"]:
             all_data.append(
                 {
                     "dataset": dataset,
-                    "strategy": r["strategy"],
-                    "lambda": r["diversity"],
-                    "ndcg": r["ndcg@10"],
-                    "mrr": r["mrr"],
-                    "ilad": r["ilad"],
-                    "latency_ms": r["latency_ms"],
+                    "strategy": run["strategy"],
+                    "lambda": run["diversity"],
+                    "ndcg": run["ndcg@10"],
+                    "mrr": run["mrr"],
+                    "ilad": run["ilad"],
+                    "latency_ms": run["latency_ms"],
                 }
             )
 
-    # Generate markdown
     md_content = generate_markdown(results)
     report_path = results_dir / "RESULTS.md"
     report_path.write_text(md_content)
     logger.debug(f"Saved: {report_path}")
 
-    # Generate plots
     pareto_path = results_dir / "pareto.png"
     generate_pareto_plot(all_data, pareto_path)
     logger.debug(f"Saved: {pareto_path}")
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index ba515a9..374207d 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -1,4 +1,4 @@
-"""Main benchmark runner."""
+"""Core benchmark execution logic."""
 
 from __future__ import annotations
 
@@ -102,7 +102,7 @@ def _evaluate_user(
     config: BenchmarkConfig,
     rng: np.random.Generator,
 ) -> list[dict]:
-    """Evaluate all strategies for a single user."""
+    """Evaluate all strategies for a single user using leave-one-out."""
     # Get user's items
     mask = data.user_ids == user_id
     profile = data.item_ids[mask]
@@ -155,7 +155,7 @@ def _evaluate_user(
 
 
 def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dict]:
-    """Aggregate per-user results into summary statistics."""
+    """Aggregate per-user results by strategy/diversity into mean and std."""
     from collections import defaultdict
 
     groups = defaultdict(list)
@@ -183,9 +183,10 @@ def _print_summary(results: list[dict]) -> None:
     print(f"{'Strategy':<10} {'λ':>5} {'nDCG@10':>10} {'ILAD':>10} {'Latency':>10}")  # noqa: T201
     print("-" * 60)  # noqa: T201
 
-    for r in results:
+    for row in results:
         print(  # noqa: T201
-            f"{r['strategy']:<10} {r['diversity']:>5.1f} {r['ndcg@10']:>10.4f} {r['ilad']:>10.4f} {r['latency_ms']:>9.2f}ms"
+            f"{row['strategy']:<10} {row['diversity']:>5.1f} "
+            f"{row['ndcg@10']:>10.4f} {row['ilad']:>10.4f} {row['latency_ms']:>9.2f}ms"
         )
 
 

From 0894ccde1ab611149c44b27792101a3fb26c3105 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 10:43:53 +0100
Subject: [PATCH 03/69] Improved benchmark code

---
 benchmarks/__main__.py    | 12 +++++++-----
 benchmarks/core/runner.py |  5 -----
 pyproject.toml            |  1 +
 uv.lock                   | 20 ++++++++++++++++++++
 4 files changed, 28 insertions(+), 10 deletions(-)

diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index ab31168..0183c28 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -70,11 +70,13 @@ def cmd_download() -> None:
                 zf.extractall(DATA_DIR)
             zip_path.unlink()
 
-            # Handle nested directories
-            for d in DATA_DIR.iterdir():
-                if d.is_dir() and d != dest_dir:
-                    if name in d.name or (name == "lastfm" and "hetrec" in d.name.lower()):
-                        d.rename(dest_dir)
+            # Rename extracted directory (zip may extract to different name)
+            for extracted_dir in DATA_DIR.iterdir():
+                if extracted_dir.is_dir() and extracted_dir != dest_dir:
+                    # Match by dataset name or known aliases
+                    dir_name_lower = extracted_dir.name.lower()
+                    if name in dir_name_lower or (name == "lastfm" and "hetrec" in dir_name_lower):
+                        extracted_dir.rename(dest_dir)
                         break
 
             logger.info(f"Saved {name} to {dest_dir}")
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index 374207d..1c1d779 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -188,8 +188,3 @@ def _print_summary(results: list[dict]) -> None:
             f"{row['strategy']:<10} {row['diversity']:>5.1f} "
             f"{row['ndcg@10']:>10.4f} {row['ilad']:>10.4f} {row['latency_ms']:>9.2f}ms"
         )
-
-
-if __name__ == "__main__":
-    # For direct testing - requires dataset to be specified
-    run_benchmark(BenchmarkConfig(dataset="ml-32m"))
diff --git a/pyproject.toml b/pyproject.toml
index c8e1882..a6c0068 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,6 +43,7 @@ dev = [
     "pytest",
     "pytest-coverage",
     "ruff",
+    "types-requests",
 ]
 benchmarks = [
     "datasets",
diff --git a/uv.lock b/uv.lock
index 4242bc4..301eeca 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2168,12 +2168,15 @@ dependencies = [
 
 [package.optional-dependencies]
 benchmarks = [
+    { name = "datasets" },
     { name = "matplotlib" },
     { name = "pandas" },
+    { name = "requests" },
     { name = "scikit-learn" },
     { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "scipy", version = "1.16.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "seaborn" },
+    { name = "tqdm" },
 ]
 dev = [
     { name = "ipython", version = "8.37.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -2183,6 +2186,7 @@ dev = [
     { name = "pytest" },
     { name = "pytest-coverage" },
     { name = "ruff" },
+    { name = "types-requests" },
 ]
 
 [package.dev-dependencies]
@@ -2194,6 +2198,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "datasets", marker = "extra == 'benchmarks'" },
     { name = "ipython", marker = "extra == 'dev'" },
     { name = "matplotlib", marker = "extra == 'benchmarks'" },
     { name = "mypy", marker = "extra == 'dev'" },
@@ -2202,10 +2207,13 @@ requires-dist = [
     { name = "pre-commit", marker = "extra == 'dev'" },
     { name = "pytest", marker = "extra == 'dev'" },
     { name = "pytest-coverage", marker = "extra == 'dev'" },
+    { name = "requests", marker = "extra == 'benchmarks'" },
     { name = "ruff", marker = "extra == 'dev'" },
     { name = "scikit-learn", marker = "extra == 'benchmarks'" },
     { name = "scipy", marker = "extra == 'benchmarks'" },
     { name = "seaborn", marker = "extra == 'benchmarks'" },
+    { name = "tqdm", marker = "extra == 'benchmarks'" },
+    { name = "types-requests", marker = "extra == 'dev'" },
 ]
 provides-extras = ["dev", "benchmarks"]
 
@@ -2630,6 +2638,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c8/0a/4aca634faf693e33004796b6cee0ae2e1dba375a800c16ab8d3eff4bb800/typer_slim-0.21.1-py3-none-any.whl", hash = "sha256:6e6c31047f171ac93cc5a973c9e617dbc5ab2bddc4d0a3135dc161b4e2020e0d", size = 47444, upload-time = "2026-01-06T11:21:12.441Z" },
 ]
 
+[[package]]
+name = "types-requests"
+version = "2.32.4.20260107"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0f/f3/a0663907082280664d745929205a89d41dffb29e89a50f753af7d57d0a96/types_requests-2.32.4.20260107.tar.gz", hash = "sha256:018a11ac158f801bfa84857ddec1650750e393df8a004a8a9ae2a9bec6fcb24f", size = 23165, upload-time = "2026-01-07T03:20:54.091Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1c/12/709ea261f2bf91ef0a26a9eed20f2623227a8ed85610c1e54c5805692ecb/types_requests-2.32.4.20260107-py3-none-any.whl", hash = "sha256:b703fe72f8ce5b31ef031264fe9395cac8f46a04661a79f7ed31a80fb308730d", size = 20676, upload-time = "2026-01-07T03:20:52.929Z" },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.15.0"

From 001153599f9a5e251a6f4ba757effa9b81a1a200 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 11:02:20 +0100
Subject: [PATCH 04/69] Added latency benchmark

---
 benchmarks/README.md           |  15 ++++++
 benchmarks/__main__.py         |  12 ++++-
 benchmarks/core/__init__.py    |   2 +
 benchmarks/core/latency.py     |  65 +++++++++++++++++++++++++
 benchmarks/core/report.py      |  84 +++++++++++++++------------------
 benchmarks/core/runner.py      |  16 +++----
 benchmarks/results/latency.png | Bin 53977 -> 78110 bytes
 7 files changed, 138 insertions(+), 56 deletions(-)
 create mode 100644 benchmarks/core/latency.py

diff --git a/benchmarks/README.md b/benchmarks/README.md
index f741e82..4ca99aa 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -59,6 +59,21 @@ python -m benchmarks report
 | **DPP** | Determinantal Point Process | O(k²·n) |
 | **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
 
+### Latency
+
+All strategies are very fast in practice. With 100 candidates and 256d embeddings (typical for modern embedding models), selecting k=10 items:
+
+| Strategy | Latency | Notes |
+|----------|---------|-------|
+| MMR | ~0.1ms | Fastest |
+| MSD | ~0.1ms | Fastest |
+| DPP | ~0.1ms | Fast |
+| SSD | ~0.5ms | Scales with embedding dimension |
+
+![Latency vs Candidates](results/latency.png)
+
+**Note:** All strategies scale linearly with `k` (items to select). MMR/MSD are O(k·n), while DPP/SSD are O(k²·n). Additionally, SSD has an O(d) factor due to Gram-Schmidt orthogonalization—if you use higher-dimensional embeddings (512d, 768d, etc.), SSD latency increases proportionally. For typical reranking scenarios (100-1000 candidates), all strategies complete in under 10ms.
+
 ### Metrics
 
 | Metric | Type | Description |
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index 0183c28..349ff10 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -114,6 +114,14 @@ def cmd_report() -> None:
     generate_report(RESULTS_DIR)
 
 
+def cmd_latency() -> None:
+    """Run latency benchmark and generate plot."""
+    from benchmarks.core.report import generate_latency_plot
+
+    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+    generate_latency_plot(RESULTS_DIR / "latency.png")
+
+
 def main() -> None:
     """Main CLI entry point."""
     parser = argparse.ArgumentParser(
@@ -124,7 +132,7 @@ def main() -> None:
     parser.add_argument(
         "command",
         nargs="?",
-        choices=["download", "run", "report"],
+        choices=["download", "run", "report", "latency"],
         default=None,
         help="Command to run (default: run)",
     )
@@ -137,6 +145,8 @@ def main() -> None:
         cmd_run()
     elif args.command == "report":
         cmd_report()
+    elif args.command == "latency":
+        cmd_latency()
     else:
         # Default: run
         cmd_run()
diff --git a/benchmarks/core/__init__.py b/benchmarks/core/__init__.py
index 05fb9d3..cc462f9 100644
--- a/benchmarks/core/__init__.py
+++ b/benchmarks/core/__init__.py
@@ -2,6 +2,7 @@
 
 from benchmarks.core.config import BenchmarkConfig
 from benchmarks.core.data import DATASET_REGISTRY, DatasetInfo, DatasetType, InteractionData, load_dataset
+from benchmarks.core.latency import run_latency_benchmark
 from benchmarks.core.metrics import ilad, ilmd, mrr, ndcg
 from benchmarks.core.report import generate_report
 from benchmarks.core.runner import run_benchmark
@@ -19,4 +20,5 @@
     "mrr",
     "ndcg",
     "run_benchmark",
+    "run_latency_benchmark",
 ]
diff --git a/benchmarks/core/latency.py b/benchmarks/core/latency.py
new file mode 100644
index 0000000..415404d
--- /dev/null
+++ b/benchmarks/core/latency.py
@@ -0,0 +1,65 @@
+"""Synthetic latency benchmark to measure algorithmic complexity."""
+
+from __future__ import annotations
+
+import time
+
+import numpy as np
+import pyversity
+
+
+def run_latency_benchmark(
+    candidate_sizes: list[int] | None = None,
+    k: int = 10,
+    embedding_dim: int = 256,
+    n_runs: int = 10,
+    seed: int = 42,
+) -> list[dict]:
+    """
+    Run synthetic latency benchmark with random embeddings.
+
+    Measures how latency scales with number of candidates for each strategy.
+    Uses 256d embeddings by default (common for modern embedding models).
+
+    Note: All strategies scale linearly with k. MMR/MSD are O(k·n), while
+    DPP/SSD are O(k²·n). Additionally, SSD has O(d) factor due to Gram-Schmidt.
+    """
+    if candidate_sizes is None:
+        candidate_sizes = [100, 250, 500, 1000, 2000, 5000]
+
+    rng = np.random.default_rng(seed)
+    strategies = [
+        pyversity.Strategy.MMR,
+        pyversity.Strategy.MSD,
+        pyversity.Strategy.DPP,
+        pyversity.Strategy.SSD,
+    ]
+
+    results = []
+    for n_candidates in candidate_sizes:
+        embeddings = rng.standard_normal((n_candidates, embedding_dim)).astype(np.float32)
+        scores = rng.uniform(0, 1, n_candidates).astype(np.float32)
+
+        for strategy in strategies:
+            latencies = []
+            for _ in range(n_runs):
+                start = time.perf_counter()
+                pyversity.diversify(
+                    embeddings=embeddings,
+                    scores=scores,
+                    k=k,
+                    strategy=strategy,
+                    diversity=0.5,
+                )
+                latencies.append((time.perf_counter() - start) * 1000)
+
+            results.append(
+                {
+                    "n_candidates": n_candidates,
+                    "strategy": strategy.value,
+                    "latency_ms": float(np.median(latencies)),
+                    "latency_std": float(np.std(latencies)),
+                }
+            )
+
+    return results
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 050d72b..12e1ed1 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -276,14 +276,13 @@ def _format_full_results(results: list[dict]) -> list[str]:
     for dataset_result in sorted(results, key=lambda x: x["dataset"]):
         dataset = dataset_result["dataset"]
         lines.append(f"### {dataset}\n")
-        lines.append("| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |")
-        lines.append("|----------|--:|--------:|----:|-----:|--------:|")
+        lines.append("| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |")
+        lines.append("|----------|--:|--------:|----:|-----:|-----:|")
 
         for run in sorted(dataset_result["results"], key=lambda x: (x["strategy"], x["diversity"])):
             lines.append(
                 f"| {run['strategy'].upper()} | {run['diversity']:.1f} | "
-                f"{run['ndcg@10']:.4f} | {run['mrr']:.4f} | {run['ilad']:.3f} | "
-                f"{run['latency_ms']:.2f}ms |"
+                f"{run['ndcg@10']:.4f} | {run['mrr']:.4f} | {run['ilad']:.3f} | {run['ilmd']:.3f} |"
             )
         lines.append("")
 
@@ -382,52 +381,48 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
     plt.close()
 
 
-def generate_latency_plot(all_data: list[dict], output_path: Path) -> None:
-    """Generate latency comparison bar chart."""
+def generate_latency_plot(output_path: Path) -> None:
+    """Generate latency scaling plot using synthetic benchmark."""
     import matplotlib.pyplot as plt
-    import numpy as np
-    import seaborn as sns
 
-    fig, ax = plt.subplots(figsize=(10, 5))
+    from benchmarks.core.latency import run_latency_benchmark
 
-    datasets = sorted(set(point["dataset"] for point in all_data))
-    strategies = ["mmr", "msd", "dpp", "ssd"]
-    colors = dict(zip(strategies, sns.color_palette("husl", len(strategies))))
+    logger.info("Running synthetic latency benchmark...")
+    results = run_latency_benchmark()
 
-    latency_by_key: dict[tuple[str, str], float] = {}
-    for point in all_data:
-        if point["lambda"] == 0.6:
-            key = (point["dataset"], point["strategy"])
-            latency_by_key[key] = point["latency_ms"]
-
-    if not latency_by_key:
-        plt.close()
-        return
+    fig, ax = plt.subplots(figsize=(10, 6))
 
-    x_labels: list[str] = []
-    bar_data: dict[str, list[float]] = {strategy: [] for strategy in strategies}
-
-    for dataset in datasets:
-        x_labels.append(dataset)
-        for strategy in strategies:
-            latency = latency_by_key.get((dataset, strategy), 0.0)
-            bar_data[strategy].append(latency)
-
-    x_positions = np.arange(len(x_labels))
-    bar_width = 0.2
-
-    for idx, (strategy, values) in enumerate(bar_data.items()):
-        ax.bar(x_positions + idx * bar_width, values, bar_width, label=strategy.upper(), color=colors[strategy])
-
-    ax.set_ylabel("Latency (ms)")
-    ax.set_title("Latency Comparison at λ=0.6")
-    ax.set_xticks(x_positions + bar_width * 1.5)
-    ax.set_xticklabels(x_labels, rotation=45, ha="right")
-    ax.legend()
-    ax.grid(axis="y", alpha=0.3)
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+    colors = {"mmr": "#e74c3c", "msd": "#2ecc71", "dpp": "#3498db", "ssd": "#9b59b6"}
+    linestyles = {"mmr": "-", "msd": "--", "dpp": "-.", "ssd": "-"}
+    markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
+
+    for strategy in strategies:
+        points = [r for r in results if r["strategy"] == strategy]
+        points = sorted(points, key=lambda x: x["n_candidates"])
+
+        x_vals = [p["n_candidates"] for p in points]
+        y_vals = [p["latency_ms"] for p in points]
+
+        ax.plot(
+            x_vals,
+            y_vals,
+            color=colors[strategy],
+            linestyle=linestyles[strategy],
+            marker=markers[strategy],
+            label=strategy.upper(),
+            markersize=8,
+            linewidth=2.5,
+        )
+
+    ax.set_xlabel("Number of Candidates (n)", fontsize=11)
+    ax.set_ylabel("Latency (ms)", fontsize=11)
+    ax.set_title("Latency Scaling by Strategy (k=10, d=256)", fontsize=12, fontweight="bold")
+    ax.legend(loc="upper left", fontsize=10)
+    ax.grid(True, alpha=0.3, linestyle="--")
 
     plt.tight_layout()
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white")
     plt.close()
 
 
@@ -454,7 +449,6 @@ def generate_report(results_dir: Path) -> None:
                     "ndcg": run["ndcg@10"],
                     "mrr": run["mrr"],
                     "ilad": run["ilad"],
-                    "latency_ms": run["latency_ms"],
                 }
             )
 
@@ -468,7 +462,7 @@ def generate_report(results_dir: Path) -> None:
     logger.debug(f"Saved: {pareto_path}")
 
     latency_path = results_dir / "latency.png"
-    generate_latency_plot(all_data, latency_path)
+    generate_latency_plot(latency_path)
     logger.debug(f"Saved: {latency_path}")
 
     logger.info(f"Report generated: {results_dir}")
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index 1c1d779..8595560 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -4,7 +4,6 @@
 
 import json
 import logging
-import time
 from datetime import datetime, timezone
 
 import numpy as np
@@ -128,7 +127,6 @@ def _evaluate_user(
 
     for strategy in config.strategies:
         for diversity in config.diversity_values:
-            start = time.perf_counter()
             result = pyversity.diversify(
                 embeddings=candidate_embeddings,
                 scores=relevance_scores,
@@ -136,14 +134,12 @@ def _evaluate_user(
                 strategy=strategy,
                 diversity=diversity,
             )
-            latency_ms = (time.perf_counter() - start) * 1000
 
             selected = candidate_ids[result.indices]
             results.append(
                 {
                     "strategy": strategy.value,
                     "diversity": diversity,
-                    "latency_ms": latency_ms,
                     "mrr": mrr(selected, test_items),
                     "ndcg@10": ndcg(selected, test_items, k=10),
                     "ilad": ilad(selected, embeddings),
@@ -166,7 +162,7 @@ def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dic
     aggregated = []
     for (strategy, diversity), group in sorted(groups.items()):
         agg = {"strategy": strategy, "diversity": diversity}
-        for metric in ["mrr", "ndcg@10", "ilad", "ilmd", "latency_ms"]:
+        for metric in ["mrr", "ndcg@10", "ilad", "ilmd"]:
             values = [r[metric] for r in group]
             agg[metric] = float(np.mean(values))
             agg[f"{metric}_std"] = float(np.std(values))
@@ -177,14 +173,14 @@ def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dic
 
 def _print_summary(results: list[dict]) -> None:
     """Print summary table (uses print for table formatting)."""
-    print("\n" + "=" * 60)  # noqa: T201
+    print("\n" + "=" * 55)  # noqa: T201
     print("RESULTS SUMMARY")  # noqa: T201
-    print("=" * 60)  # noqa: T201
-    print(f"{'Strategy':<10} {'λ':>5} {'nDCG@10':>10} {'ILAD':>10} {'Latency':>10}")  # noqa: T201
-    print("-" * 60)  # noqa: T201
+    print("=" * 55)  # noqa: T201
+    print(f"{'Strategy':<10} {'λ':>5} {'nDCG@10':>10} {'MRR':>10} {'ILAD':>10}")  # noqa: T201
+    print("-" * 55)  # noqa: T201
 
     for row in results:
         print(  # noqa: T201
             f"{row['strategy']:<10} {row['diversity']:>5.1f} "
-            f"{row['ndcg@10']:>10.4f} {row['ilad']:>10.4f} {row['latency_ms']:>9.2f}ms"
+            f"{row['ndcg@10']:>10.4f} {row['mrr']:>10.4f} {row['ilad']:>10.4f}"
         )
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index 5896ae7250abe1e30be59decb8e67b244a3edcbf..5dea65d54b98f41cfe9fcd82f0dad54c13a626e6 100644
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z97KAHhp}4zb70{qFS_}kLu95Osk#B$-v($LTcwKQ28rl9R6ZN4QCC$TQ+4Y9`Tw@H
cs`>m?{pz_ZEQ)_zNZ;Sp$=$KYer@9a0fJZN!vFvP


From c578a72d75d741f63c109b625f41ca405f269c45 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 11:07:31 +0100
Subject: [PATCH 05/69] Updated benchmarks

---
 benchmarks/README.md | 107 ++++++++++++++++++++++---------------------
 1 file changed, 56 insertions(+), 51 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 4ca99aa..5983d7d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -3,27 +3,51 @@
 Comprehensive evaluation of diversification strategies across 4 recommendation datasets.
 We measure the relevance-diversity tradeoff to help you choose the right strategy for your use case.
 
-## Key Finding
+## Table of Contents
+
+- [Key Findings](#key-findings)
+- [Relevance-Diversity Tradeoff](#relevance-diversity-tradeoff)
+- [Latency](#latency)
+- [Usage](#usage)
+- [Datasets](#datasets)
+- [Methodology](#methodology)
+- [Citations](#citations)
+
+## Key Findings
 
 **Different strategies excel at different diversity levels:**
 
-| Diversity Level | Winner | Recommendation |
-|-----------------|--------|----------------|
-| Low (ILAD 0.3-0.5) | **SSD** | Best for light diversification |
-| Moderate (ILAD 0.5-0.7) | **MSD/DPP** | Good middle-ground |
-| High (ILAD 0.7-0.9) | **MSD** | Best for heavy diversification |
-| Maximum (ILAD 0.9+) | **MSD** | Dominates at extreme diversity |
+| Diversity Level | Winner | λ | Notes |
+|-----------------|--------|---|-------|
+| Low (ILAD 0.3-0.5) | **SSD** | 0.6-0.8 | Highest relevance at light diversification |
+| Moderate (ILAD 0.5-0.7) | **MSD/DPP** | 0.4-0.6 | Good middle-ground |
+| High (ILAD 0.7-0.9) | **MSD** | 0.4-0.6 | Best for heavy diversification |
+| Maximum (ILAD 0.9+) | **MSD** | 0.2-0.4 | Dominates at extreme diversity |
+
+## Relevance-Diversity Tradeoff
 
 ![Relevance vs Diversity Tradeoff](results/pareto.png)
 
-## Quick Recommendations
+## Latency
+
+All strategies are extremely fast in practice. The plot below shows latency scaling with the number of candidates (k=10, d=256):
+
+![Latency vs Candidates](results/latency.png)
+
+**Key observations:**
+- **All strategies are very fast**: Even with 10,000 candidates, all strategies complete in under 100ms
+- **Typical use case**: With 100 candidates (common for reranking), all strategies complete in <1ms
+- **MMR/MSD/DPP** are nearly identical in speed for practical candidate sizes
+- **SSD** is slower due to Gram-Schmidt orthogonalization—scales with embedding dimension d
+
+| Strategy | 100 candidates | 1,000 candidates | 10,000 candidates |
+|----------|----------------|------------------|-------------------|
+| MMR | ~0.1ms | ~1ms | ~10ms |
+| MSD | ~0.1ms | ~1ms | ~10ms |
+| DPP | ~0.1ms | ~2ms | ~20ms |
+| SSD | ~0.5ms | ~5ms | ~80ms |
 
-| Your Goal | Strategy | λ | Notes |
-|-----------|----------|---|-------|
-| Light diversification | **SSD** | 0.6-0.8 | Highest relevance at moderate diversity |
-| Balanced tradeoff | **DPP** | 0.4-0.6 | Good across multiple diversity levels |
-| Maximum diversity | **MSD** | 0.4-0.6 | Best when diversity is the priority |
-| Speed-critical | **MMR** | 0.6-0.8 | Fastest (O(k·n) complexity) |
+*Measured with k=10 items selected, d=256 dimensional embeddings.*
 
 ## Usage
 
@@ -41,15 +65,6 @@ python -m benchmarks report
 <details>
 <summary>Detailed Results</summary>
 
-### Datasets
-
-| Dataset | Domain | Interactions | Source |
-|---------|--------|--------------|--------|
-| MovieLens-32M | Movies | 32M ratings | [GroupLens](https://grouplens.org/datasets/movielens/32m/) |
-| Last.FM | Music | 92K plays | [HetRec 2011](http://ir.ii.uam.es/hetrec2011/) |
-| Amazon Video Games | Games | 47K reviews | [UCSD](https://cseweb.ucsd.edu/~jmcauley/datasets.html) |
-| Goodreads | Books | 869K ratings | [UCSD](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/) |
-
 ### Strategies
 
 | Strategy | Description | Complexity |
@@ -57,22 +72,7 @@ python -m benchmarks report
 | **MMR** | Maximal Marginal Relevance | O(k·n) |
 | **MSD** | Max-Sum Diversification | O(k·n) |
 | **DPP** | Determinantal Point Process | O(k²·n) |
-| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
-
-### Latency
-
-All strategies are very fast in practice. With 100 candidates and 256d embeddings (typical for modern embedding models), selecting k=10 items:
-
-| Strategy | Latency | Notes |
-|----------|---------|-------|
-| MMR | ~0.1ms | Fastest |
-| MSD | ~0.1ms | Fastest |
-| DPP | ~0.1ms | Fast |
-| SSD | ~0.5ms | Scales with embedding dimension |
-
-![Latency vs Candidates](results/latency.png)
-
-**Note:** All strategies scale linearly with `k` (items to select). MMR/MSD are O(k·n), while DPP/SSD are O(k²·n). Additionally, SSD has an O(d) factor due to Gram-Schmidt orthogonalization—if you use higher-dimensional embeddings (512d, 768d, etc.), SSD latency increases proportionally. For typical reranking scenarios (100-1000 candidates), all strategies complete in under 10ms.
+| **SSD** | Sliding Spectrum Decomposition | O(k²·n·d) |
 
 ### Metrics
 
@@ -81,7 +81,6 @@ All strategies are very fast in practice. With 100 candidates and 256d embedding
 | nDCG@10 | Relevance | Normalized Discounted Cumulative Gain |
 | MRR | Relevance | Mean Reciprocal Rank |
 | ILAD | Diversity | Intra-List Average Distance |
-| Latency | Efficiency | Time per diversification call |
 
 ### Per-Dataset Winners
 
@@ -92,7 +91,22 @@ All strategies are very fast in practice. With 100 candidates and 256d embedding
 | Amazon-VG | - | - | MMR | MSD |
 | Goodreads | SSD | MSD | MSD | MSD |
 
-### Methodology
+### Full Results
+
+See [`results/RESULTS.md`](results/RESULTS.md) for complete tables.
+
+</details>
+
+## Datasets
+
+| Dataset | Domain | Interactions | Source |
+|---------|--------|--------------|--------|
+| MovieLens-32M | Movies | 32M ratings | [GroupLens](https://grouplens.org/datasets/movielens/32m/) |
+| Last.FM | Music | 92K plays | [HetRec 2011](http://ir.ii.uam.es/hetrec2011/) |
+| Amazon Video Games | Games | 47K reviews | [UCSD](https://cseweb.ucsd.edu/~jmcauley/datasets.html) |
+| Goodreads | Books | 869K ratings | [UCSD](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/) |
+
+## Methodology
 
 - **Evaluation**: Leave-one-out protocol with 2,000 sampled users per dataset
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
@@ -100,12 +114,6 @@ All strategies are very fast in practice. With 100 candidates and 256d embedding
 - **Selection**: k=20 items selected by each strategy
 - **λ sweep**: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0
 
-### Full Results
-
-See [`results/RESULTS.md`](results/RESULTS.md) for complete tables.
-
-</details>
-
 <details>
 <summary>Programmatic API</summary>
 
@@ -124,8 +132,7 @@ results = run_benchmark(config)
 
 </details>
 
-<details>
-<summary>Citations</summary>
+## Citations
 
 ```bibtex
 @article{harper2015movielens,
@@ -152,5 +159,3 @@ results = run_benchmark(config)
   booktitle={RecSys}, year={2018}
 }
 ```
-
-</details>

From b465b3c436bb473fb44d05f383d1dcd976e1f9ce Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 11:12:55 +0100
Subject: [PATCH 06/69] Updated benchmarks

---
 benchmarks/README.md | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 5983d7d..175dcdd 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -1,7 +1,17 @@
 # Pyversity Benchmarks
 
-Comprehensive evaluation of diversification strategies across 4 recommendation datasets.
-We measure the relevance-diversity tradeoff to help you choose the right strategy for your use case.
+This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**, and **SSD** across 4 recommendation datasets.
+
+For each dataset, we use a leave-one-out evaluation protocol (meaning we hold out one item
+per user as the test set), generate candidate items based on similar items, then rerank with each
+diversification strategy. We measure relevance (nDCG, MRR) and diversity (ILAD) to trace the
+Pareto frontier of the relevance-diversity tradeoff.
+
+In addition, we measure latency of each strategy as the number of candidates scales.
+
+> **Note:** We don't benchmark COVER (coverage-based diversification) because it optimizes a different
+> objective (topic/category coverage) and requires explicit item taxonomies not available in standard
+> collaborative filtering datasets.
 
 ## Table of Contents
 

From 6f597a06937ba38da3bdc4295604ecb03ff19033 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 12:53:56 +0100
Subject: [PATCH 07/69] Added ILMD to benchmarks

---
 benchmarks/README.md                          |  56 +-
 benchmarks/__main__.py                        |   2 +-
 benchmarks/core/config.py                     |   4 +-
 benchmarks/core/report.py                     | 486 +++++++++++-----
 benchmarks/results/RESULTS.md                 | 402 +++++++------
 .../amazon-product-reviews-video-games.json   | 351 -----------
 benchmarks/results/amazon-video-games.json    | 543 ++++++++++++++++++
 benchmarks/results/goodreads-rating.json      | 351 -----------
 benchmarks/results/goodreads.json             | 543 ++++++++++++++++++
 benchmarks/results/lastfm.json                | 338 ++++++++---
 benchmarks/results/latency.png                | Bin 78110 -> 75566 bytes
 benchmarks/results/ml-32m.json                | 338 ++++++++---
 benchmarks/results/pareto.png                 | Bin 334205 -> 0 bytes
 benchmarks/results/pareto_ilad.png            | Bin 0 -> 298105 bytes
 benchmarks/results/pareto_ilmd.png            | Bin 0 -> 311456 bytes
 15 files changed, 2245 insertions(+), 1169 deletions(-)
 delete mode 100644 benchmarks/results/amazon-product-reviews-video-games.json
 create mode 100644 benchmarks/results/amazon-video-games.json
 delete mode 100644 benchmarks/results/goodreads-rating.json
 create mode 100644 benchmarks/results/goodreads.json
 delete mode 100644 benchmarks/results/pareto.png
 create mode 100644 benchmarks/results/pareto_ilad.png
 create mode 100644 benchmarks/results/pareto_ilmd.png

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 175dcdd..8b769cb 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -4,7 +4,7 @@ This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**,
 
 For each dataset, we use a leave-one-out evaluation protocol (meaning we hold out one item
 per user as the test set), generate candidate items based on similar items, then rerank with each
-diversification strategy. We measure relevance (nDCG, MRR) and diversity (ILAD) to trace the
+diversification strategy. We measure relevance (nDCG, MRR) and diversity (ILAD, ILMD) to trace the
 Pareto frontier of the relevance-diversity tradeoff.
 
 In addition, we measure latency of each strategy as the number of candidates scales.
@@ -25,18 +25,49 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-**Different strategies excel at different diversity levels:**
+We evaluate using two diversity metrics that capture different aspects:
+- **ILAD** (Intra-List Average Distance): Measures overall diversity spread
+- **ILMD** (Intra-List Minimum Distance): Ensures no very similar item pairs (worst-case diversity)
 
-| Diversity Level | Winner | λ | Notes |
-|-----------------|--------|---|-------|
-| Low (ILAD 0.3-0.5) | **SSD** | 0.6-0.8 | Highest relevance at light diversification |
-| Moderate (ILAD 0.5-0.7) | **MSD/DPP** | 0.4-0.6 | Good middle-ground |
-| High (ILAD 0.7-0.9) | **MSD** | 0.4-0.6 | Best for heavy diversification |
-| Maximum (ILAD 0.9+) | **MSD** | 0.2-0.4 | Dominates at extreme diversity |
+### Summary: Pareto Area Under Curve
+
+The Pareto area measures the total relevance-diversity tradeoff quality by computing the area under each strategy's curve. Higher area = better overall performance across all diversity levels.
+
+| Metric | Best | 2nd | 3rd | 4th |
+|--------|------|-----|-----|-----|
+| **nDCG vs ILAD** | MSD | DPP | SSD | MMR |
+| **nDCG vs ILMD** | MMR* | DPP | SSD | MSD |
+
+*\*MMR "wins" ILMD by not diversifying—it keeps items similar (high relevance, low diversity). For actual diversification, use DPP or SSD.*
+
+**Key insight:** The "best" strategy depends on your diversity metric:
+- **MSD** maximizes average pairwise distance—great for overall variety
+- **DPP/SSD** better ensure no very similar pairs slip through
+- **MMR** achieves high ILMD by barely diversifying (not recommended if diversity matters)
+
+### Recommendations
+
+| Goal | Strategy | λ | Why |
+|-----------|----------|---|-----|
+| Maximum relevance, light diversity | **SSD** | 0.7-0.9 | Best nDCG at low ILAD |
+| Balanced tradeoff | **DPP** | 0.4-0.6 | Good on both ILAD and ILMD |
+| High average diversity | **MSD** | 0.3-0.5 | Dominates ILAD metric |
+| No similar pairs (strict) | **DPP** | 0.3-0.5 | Best ILMD scores |
+| Extreme diversity | **MSD** | 0.1-0.3 | Only strategy reaching ILAD > 0.9 |
 
 ## Relevance-Diversity Tradeoff
 
-![Relevance vs Diversity Tradeoff](results/pareto.png)
+### ILAD (Average Diversity)
+
+![Relevance vs ILAD Tradeoff](results/pareto_ilad.png)
+
+*Higher ILAD = more overall variety in recommendations*
+
+### ILMD (Minimum Diversity)
+
+![Relevance vs ILMD Tradeoff](results/pareto_ilmd.png)
+
+*Higher ILMD = no very similar pairs (stricter diversity guarantee)*
 
 ## Latency
 
@@ -88,9 +119,10 @@ python -m benchmarks report
 
 | Metric | Type | Description |
 |--------|------|-------------|
-| nDCG@10 | Relevance | Normalized Discounted Cumulative Gain |
+| nDCG@k | Relevance | Normalized Discounted Cumulative Gain |
 | MRR | Relevance | Mean Reciprocal Rank |
-| ILAD | Diversity | Intra-List Average Distance |
+| ILAD | Diversity | Intra-List Average Distance (mean pairwise) |
+| ILMD | Diversity | Intra-List Minimum Distance (worst-case pair) |
 
 ### Per-Dataset Winners
 
@@ -122,7 +154,7 @@ See [`results/RESULTS.md`](results/RESULTS.md) for complete tables.
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=20 items selected by each strategy
-- **λ sweep**: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0
+- **λ sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
 <details>
 <summary>Programmatic API</summary>
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index 349ff10..e9a339e 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -15,7 +15,7 @@
 import zipfile
 from pathlib import Path
 
-import requests
+import requests  # type: ignore[import-untyped]
 from tqdm import tqdm
 
 from benchmarks.core import (
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index 4410827..1ff9146 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -33,7 +33,9 @@ class BenchmarkConfig:
 
     # Strategies
     strategies: list[Strategy] = field(default_factory=lambda: [Strategy.MMR, Strategy.MSD, Strategy.DPP, Strategy.SSD])
-    diversity_values: list[float] = field(default_factory=lambda: [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+    diversity_values: list[float] = field(
+        default_factory=lambda: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+    )
 
     # Output
     output_dir: Path = Path("benchmarks/results")
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 12e1ed1..f221093 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -41,15 +41,8 @@ def _compute_retention_table(results: list[dict], retention_pct: float = 0.95) -
     return table
 
 
-def _compute_regional_winners(results: list[dict]) -> dict:
-    """Compute which strategy achieves best nDCG in each diversity region."""
-    regions = {
-        "Low (0.3-0.5)": (0.3, 0.5),
-        "Moderate (0.5-0.7)": (0.5, 0.7),
-        "High (0.7-0.9)": (0.7, 0.9),
-        "Maximum (0.9+)": (0.9, 1.0),
-    }
-
+def _extract_all_points(results: list[dict]) -> list[dict]:
+    """Extract all data points from results into flat list."""
     all_points = []
     for dataset_result in results:
         dataset = dataset_result["dataset"]
@@ -62,16 +55,24 @@ def _compute_regional_winners(results: list[dict]) -> dict:
                     "ilad": run["ilad"],
                 }
             )
+    return all_points
 
+
+def _find_regional_winners(
+    datasets: list[str],
+    all_points: list[dict],
+    regions: dict[str, tuple[float, float]],
+) -> tuple[dict, dict]:
+    """Find winner per region per dataset and aggregate wins."""
     regional_data: dict[str, dict[str, dict[str, str | float]]] = {}
-    datasets = sorted(set(point["dataset"] for point in all_points))
+    region_wins: dict[str, dict[str, int]] = {region: {} for region in regions}
 
     for dataset in datasets:
-        dataset_points = [point for point in all_points if point["dataset"] == dataset]
+        dataset_points = [p for p in all_points if p["dataset"] == dataset]
         regional_data[dataset] = {}
 
         for region_name, (lo, hi) in regions.items():
-            in_region = [point for point in dataset_points if lo <= point["ilad"] < hi]
+            in_region = [p for p in dataset_points if lo <= p["ilad"] < hi]
             if in_region:
                 best = max(in_region, key=lambda x: x["ndcg"])
                 regional_data[dataset][region_name] = {
@@ -79,66 +80,64 @@ def _compute_regional_winners(results: list[dict]) -> dict:
                     "ndcg": best["ndcg"],
                     "ilad": best["ilad"],
                 }
+                winner = str(best["strategy"])
+                region_wins[region_name][winner] = region_wins[region_name].get(winner, 0) + 1
 
-    region_wins: dict[str, dict[str, int]] = {region: {} for region in regions}
-    for dataset, regions_dict in regional_data.items():
-        for region_name, data in regions_dict.items():
-            winner = str(data["winner"])
-            region_wins[region_name][winner] = region_wins[region_name].get(winner, 0) + 1
+    return regional_data, region_wins
 
-    return {"per_dataset": regional_data, "aggregate": region_wins}
 
+def _compute_region_averages(
+    datasets: list[str],
+    all_points: list[dict],
+    regions: dict[str, tuple[float, float]],
+) -> dict[str, dict[str, float]]:
+    """Compute average best nDCG per strategy per region across datasets."""
+    all_strategies = {"ssd", "dpp", "msd", "mmr"}
+    region_best_per_dataset: dict[str, dict[str, dict[str, float]]] = {r: {} for r in regions}
 
-def _compute_f1_scores(results: list[dict]) -> tuple[dict, dict]:
-    """Compute normalized F1 scores (harmonic mean of nDCG and ILAD)."""
-    best_per_dataset: dict[str, dict[str, dict[str, float]]] = {}
-    all_scores: list[dict[str, str | float]] = []
+    for dataset in datasets:
+        dataset_points = [p for p in all_points if p["dataset"] == dataset]
+        for region_name, (lo, hi) in regions.items():
+            in_region = [p for p in dataset_points if lo <= p["ilad"] < hi]
+            strategy_best: dict[str, float] = {}
+            for point in in_region:
+                s = point["strategy"]
+                if s not in strategy_best or point["ndcg"] > strategy_best[s]:
+                    strategy_best[s] = point["ndcg"]
+            if strategy_best:
+                region_best_per_dataset[region_name][dataset] = strategy_best
+
+    region_avg: dict[str, dict[str, float]] = {}
+    for region, dataset_bests in region_best_per_dataset.items():
+        valid = [d for d in dataset_bests.values() if set(d.keys()) == all_strategies]
+        if not valid:
+            valid = list(dataset_bests.values())
+
+        strategy_scores: dict[str, list[float]] = {}
+        for ds_bests in valid:
+            for s, score in ds_bests.items():
+                strategy_scores.setdefault(s, []).append(score)
+        region_avg[region] = {s: sum(v) / len(v) for s, v in strategy_scores.items() if v}
+
+    return region_avg
 
-    for dataset_result in results:
-        dataset = dataset_result["dataset"]
-        best_per_dataset[dataset] = {}
 
-        ndcgs = [run["ndcg@10"] for run in dataset_result["results"]]
-        ilads = [run["ilad"] for run in dataset_result["results"]]
-        ndcg_min, ndcg_max = min(ndcgs), max(ndcgs)
-        ilad_min, ilad_max = min(ilads), max(ilads)
+def _compute_regional_winners(results: list[dict]) -> dict:
+    """Compute which strategy achieves best nDCG in each diversity region."""
+    regions = {
+        "Low (0.3-0.5)": (0.3, 0.5),
+        "Moderate (0.5-0.7)": (0.5, 0.7),
+        "High (0.7-0.9)": (0.7, 0.9),
+        "Maximum (0.9+)": (0.9, 1.0),
+    }
 
-        for strategy in STRATEGIES:
-            runs = [
-                run for run in dataset_result["results"] if run["strategy"] == strategy and 0 < run["diversity"] < 1
-            ]
-
-            best_f1 = 0.0
-            best_run = None
-
-            for run in runs:
-                ndcg_norm = (run["ndcg@10"] - ndcg_min) / (ndcg_max - ndcg_min) if ndcg_max > ndcg_min else 0.0
-                ilad_norm = (run["ilad"] - ilad_min) / (ilad_max - ilad_min) if ilad_max > ilad_min else 0.0
-
-                if ndcg_norm + ilad_norm > 0:
-                    f1 = 2 * ndcg_norm * ilad_norm / (ndcg_norm + ilad_norm)
-                else:
-                    f1 = 0.0
-
-                if f1 > best_f1:
-                    best_f1 = f1
-                    best_run = run
-
-            if best_run:
-                best_per_dataset[dataset][strategy] = {
-                    "f1": best_f1,
-                    "ndcg": best_run["ndcg@10"],
-                    "ilad": best_run["ilad"],
-                    "lambda": best_run["diversity"],
-                }
-                all_scores.append({"strategy": strategy, "f1": best_f1})
+    all_points = _extract_all_points(results)
+    datasets = sorted(set(p["dataset"] for p in all_points))
 
-    strategy_avg: dict[str, float] = {}
-    for strategy in STRATEGIES:
-        scores = [float(entry["f1"]) for entry in all_scores if entry["strategy"] == strategy]
-        strategy_avg[strategy] = sum(scores) / len(scores) if scores else 0.0
+    regional_data, region_wins = _find_regional_winners(datasets, all_points, regions)
+    region_avg = _compute_region_averages(datasets, all_points, regions)
 
-    return best_per_dataset, strategy_avg
+    return {"per_dataset": regional_data, "aggregate": region_wins, "avg_ndcg": region_avg}
 
 
 def _format_retention_table(retention_table: dict) -> list[str]:
@@ -172,53 +171,6 @@ def _format_retention_table(retention_table: dict) -> list[str]:
     return lines
 
 
-def _format_ranking_table(f1_ranking: dict) -> list[str]:
-    """Format the F1 ranking table as markdown lines."""
-    lines = [
-        "## Overall Strategy Ranking\n",
-        "Ranking by average F1 score (harmonic mean of normalized nDCG and ILAD):\n",
-        "| Rank | Strategy | Avg F1 | Interpretation |",
-        "|:----:|----------|:------:|----------------|",
-    ]
-
-    sorted_ranking = sorted(f1_ranking.items(), key=lambda x: -x[1])
-    interpretations = {
-        "msd": "Best diversity with acceptable relevance",
-        "dpp": "Good balance of both metrics",
-        "mmr": "Conservative, relevance-focused",
-        "ssd": "Conservative, relevance-focused",
-    }
-    rank_labels = {0: "1.", 1: "2.", 2: "3."}
-
-    for idx, (strategy, score) in enumerate(sorted_ranking):
-        rank = rank_labels.get(idx, f"{idx + 1}.")
-        lines.append(f"| {rank} | **{strategy.upper()}** | {score:.3f} | {interpretations.get(strategy, '')} |")
-
-    lines.append("")
-    return lines
-
-
-def _format_f1_details(f1_per_dataset: dict) -> list[str]:
-    """Format the detailed F1 table as markdown lines."""
-    lines = [
-        "## Best F1 Score per Dataset\n",
-        "| Dataset | Strategy | λ | nDCG@10 | ILAD | F1 |",
-        "|---------|----------|--:|--------:|-----:|---:|",
-    ]
-
-    for dataset in sorted(f1_per_dataset.keys()):
-        for strategy in STRATEGIES:
-            if strategy in f1_per_dataset[dataset]:
-                entry = f1_per_dataset[dataset][strategy]
-                lines.append(
-                    f"| {dataset} | {strategy.upper()} | {entry['lambda']:.1f} | "
-                    f"{entry['ndcg']:.4f} | {entry['ilad']:.3f} | {entry['f1']:.3f} |"
-                )
-
-    lines.append("")
-    return lines
-
-
 def _format_regional_analysis(regional_data: dict) -> list[str]:
     """Format the regional winner analysis as markdown."""
     lines = [
@@ -240,32 +192,271 @@ def _format_regional_analysis(regional_data: dict) -> list[str]:
                 row.append("-")
         lines.append("| " + " | ".join(row) + " |")
 
-    # Add summary row
+    # Add summary using best nDCG per region (averaged across datasets)
     lines.append("")
-    lines.append("### Summary: Wins per Strategy by Region\n")
-    lines.append("| Region | SSD | DPP | MSD | MMR | Recommendation |")
-    lines.append("|--------|:---:|:---:|:---:|:---:|----------------|")
-
-    recommendations = {
-        "Low (0.3-0.5)": "Use **SSD** for light diversification",
-        "Moderate (0.5-0.7)": "Use **MSD** or **DPP** for moderate diversity",
-        "High (0.7-0.9)": "Use **MSD** for heavy diversification",
-        "Maximum (0.9+)": "Use **MSD** for maximum diversity",
-    }
+    lines.append("### Summary: Best nDCG per Strategy by Region\n")
+    lines.append("For each region, what's the best nDCG each strategy can achieve (averaged across datasets)?\n")
+    lines.append("| Region | SSD | DPP | MSD | MMR | Best |")
+    lines.append("|--------|:---:|:---:|:---:|:---:|:----:|")
+
+    avg_ndcg = regional_data.get("avg_ndcg", {})
 
     for region in region_order:
-        wins = regional_data["aggregate"].get(region, {})
+        region_avgs = avg_ndcg.get(region, {})
         row = [region]
+        best_strategy = ""
+        best_val = -1.0
+        for s in ["ssd", "dpp", "msd", "mmr"]:
+            val = region_avgs.get(s, 0)
+            if val > 0:
+                row.append(f"{val:.3f}")
+                if val > best_val:
+                    best_val = val
+                    best_strategy = s.upper()
+            else:
+                row.append("-")
+        row.append(f"**{best_strategy}**" if best_strategy else "-")
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.append("")
+    return lines
+
+
+def _trapezoidal_area(points: list[dict]) -> float:
+    """Compute area under curve using trapezoidal rule."""
+    if len(points) < 2:
+        return 0.0
+    sorted_pts = sorted(points, key=lambda p: p["ilad"])
+    area = 0.0
+    for i in range(len(sorted_pts) - 1):
+        dx = sorted_pts[i + 1]["ilad"] - sorted_pts[i]["ilad"]
+        avg_y = (sorted_pts[i]["ndcg"] + sorted_pts[i + 1]["ndcg"]) / 2
+        area += dx * avg_y
+    return area
+
+
+def _find_overlap_range(strategy_points: dict[str, list[dict]]) -> tuple[float, float]:
+    """Find ILAD range where all strategies have data points."""
+    max_mins = []
+    min_maxs = []
+    for points in strategy_points.values():
+        if points:
+            ilads = [p["ilad"] for p in points]
+            max_mins.append(min(ilads))
+            min_maxs.append(max(ilads))
+
+    if max_mins and min_maxs:
+        return max(max_mins), min(min_maxs)
+    return 0.0, 1.0
+
+
+def _compute_pareto_area(results: list[dict]) -> dict:
+    """
+    Compute area under each strategy's Pareto curve per dataset.
+
+    Uses trapezoidal integration, sorted by ILAD.
+    Higher area = better overall relevance-diversity tradeoff.
+    """
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+
+    # Collect points per dataset per strategy
+    dataset_strategy_points: dict[str, dict[str, list[dict]]] = {}
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        dataset_strategy_points[dataset] = {s: [] for s in strategies}
+        for run in dataset_result["results"]:
+            dataset_strategy_points[dataset][run["strategy"]].append({"ndcg": run["ndcg@10"], "ilad": run["ilad"]})
+
+    full_areas: dict[str, dict[str, float]] = {s: {} for s in strategies}
+    overlap_areas: dict[str, dict[str, float]] = {s: {} for s in strategies}
+
+    for dataset, strategy_points in dataset_strategy_points.items():
+        overlap_lo, overlap_hi = _find_overlap_range(strategy_points)
+        for s, points in strategy_points.items():
+            full_areas[s][dataset] = _trapezoidal_area(points)
+            overlap_pts = [p for p in points if overlap_lo <= p["ilad"] <= overlap_hi]
+            overlap_areas[s][dataset] = _trapezoidal_area(overlap_pts)
+
+    # Average across datasets
+    avg_full = {s: sum(full_areas[s].values()) / len(full_areas[s]) if full_areas[s] else 0.0 for s in strategies}
+    avg_overlap = {
+        s: sum(overlap_areas[s].values()) / len(overlap_areas[s]) if overlap_areas[s] else 0.0 for s in strategies
+    }
+
+    return {
+        "full_per_dataset": full_areas,
+        "overlap_per_dataset": overlap_areas,
+        "avg_full": avg_full,
+        "avg_overlap": avg_overlap,
+    }
+
+
+def _compute_pareto_dominance(results: list[dict]) -> dict:
+    """
+    Compute Pareto frontier dominance for each strategy.
+
+    A point is on the Pareto frontier if no other point has both higher nDCG
+    and higher ILAD. Returns count of frontier points per strategy per dataset.
+    """
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+
+    # Collect all points per dataset
+    dataset_points: dict[str, list[dict]] = {}
+    for dataset_result in results:
+        dataset = dataset_result["dataset"]
+        dataset_points[dataset] = []
+        for run in dataset_result["results"]:
+            dataset_points[dataset].append(
+                {
+                    "strategy": run["strategy"],
+                    "ndcg": run["ndcg@10"],
+                    "ilad": run["ilad"],
+                }
+            )
+
+    # Compute Pareto frontier for each dataset
+    frontier_counts: dict[str, dict[str, int]] = {s: {} for s in strategies}
+    total_frontier: dict[str, int] = {s: 0 for s in strategies}
+
+    for dataset, points in dataset_points.items():
+        for point in points:
+            is_dominated = False
+            for other in points:
+                if other is point:
+                    continue
+                # Check if 'other' dominates 'point' (better or equal on both, strictly better on at least one)
+                if other["ndcg"] >= point["ndcg"] and other["ilad"] >= point["ilad"]:
+                    if other["ndcg"] > point["ndcg"] or other["ilad"] > point["ilad"]:
+                        is_dominated = True
+                        break
+
+            if not is_dominated:
+                strategy = point["strategy"]
+                frontier_counts[strategy][dataset] = frontier_counts[strategy].get(dataset, 0) + 1
+                total_frontier[strategy] += 1
+
+    # Compute percentage of all frontier points
+    total_points = sum(total_frontier.values())
+    frontier_pct: dict[str, float] = {
+        s: (count / total_points * 100) if total_points > 0 else 0.0 for s, count in total_frontier.items()
+    }
+
+    return {
+        "per_dataset": frontier_counts,
+        "total": total_frontier,
+        "percentage": frontier_pct,
+    }
+
+
+def _compute_multi_metric_summary(results: list[dict]) -> dict:
+    """
+    Compute Pareto area for multiple metric combinations.
+
+    Analyzes: nDCG vs ILAD, MRR vs ILAD, nDCG vs ILMD, MRR vs ILMD
+    """
+    strategies = ["mmr", "msd", "dpp", "ssd"]
+    metric_pairs = [
+        ("ndcg@10", "ilad", "nDCG vs ILAD"),
+        ("mrr", "ilad", "MRR vs ILAD"),
+        ("ndcg@10", "ilmd", "nDCG vs ILMD"),
+        ("mrr", "ilmd", "MRR vs ILMD"),
+    ]
+
+    def _compute_area(points: list[dict], x_key: str, y_key: str) -> float:
+        """Compute area under curve using trapezoidal rule."""
+        if len(points) < 2:
+            return 0.0
+        sorted_pts = sorted(points, key=lambda p: p[x_key])
+        area = 0.0
+        for i in range(len(sorted_pts) - 1):
+            dx = sorted_pts[i + 1][x_key] - sorted_pts[i][x_key]
+            avg_y = (sorted_pts[i][y_key] + sorted_pts[i + 1][y_key]) / 2
+            area += dx * avg_y
+        return area
+
+    summary: dict[str, dict[str, float]] = {label: {} for _, _, label in metric_pairs}
+
+    for rel_key, div_key, label in metric_pairs:
+        strategy_areas: dict[str, list[float]] = {s: [] for s in strategies}
+
+        for dataset_result in results:
+            for s in strategies:
+                points = [
+                    {rel_key: run[rel_key], div_key: run[div_key]}
+                    for run in dataset_result["results"]
+                    if run["strategy"] == s
+                ]
+                area = _compute_area(points, div_key, rel_key)
+                strategy_areas[s].append(area)
+
+        # Average across datasets
+        for s in strategies:
+            summary[label][s] = sum(strategy_areas[s]) / len(strategy_areas[s]) if strategy_areas[s] else 0.0
+
+    return summary
+
+
+def _format_multi_metric_table(multi_metric: dict) -> list[str]:
+    """Format multi-metric summary as markdown table."""
+    lines = [
+        "### Multi-Metric Comparison\n",
+        "Area under curve for different relevance-diversity metric combinations:\n",
+        "| Metrics | SSD | DPP | MSD | MMR | Best |",
+        "|---------|:---:|:---:|:---:|:---:|:----:|",
+    ]
+
+    for label, areas in multi_metric.items():
+        row = [label]
+        best_s = ""
+        best_val = -1.0
         for s in ["ssd", "dpp", "msd", "mmr"]:
-            count = wins.get(s, 0)
-            row.append(str(count) if count > 0 else "-")
-        row.append(recommendations.get(region, ""))
+            val = areas.get(s, 0)
+            row.append(f"{val:.4f}")
+            if val > best_val:
+                best_val = val
+                best_s = s.upper()
+        row.append(f"**{best_s}**")
         lines.append("| " + " | ".join(row) + " |")
 
+    lines.append("")
+    lines.append("*Higher area = better overall tradeoff between that relevance and diversity metric.*")
     lines.append("")
     return lines
 
 
+def _format_pareto_summary(pareto_data: dict, area_data: dict) -> list[str]:
+    """Format Pareto dominance and area summary as markdown."""
+    lines = [
+        "## Overall Performance Summary\n",
+        "### Pareto Frontier Analysis\n",
+        "Points on the Pareto frontier represent optimal relevance-diversity tradeoffs.\n",
+        "| Strategy | Frontier Points | Share | Avg Area (Full) | Avg Area (Overlap) |",
+        "|----------|----------------:|------:|----------------:|-------------------:|",
+    ]
+
+    total = pareto_data["total"]
+    pct = pareto_data["percentage"]
+    avg_full = area_data["avg_full"]
+    avg_overlap = area_data["avg_overlap"]
+
+    # Sort by overlap area descending (fairest comparison)
+    for strategy in sorted(total.keys(), key=lambda s: avg_overlap.get(s, 0), reverse=True):
+        lines.append(
+            f"| **{strategy.upper()}** | {total[strategy]} | {pct[strategy]:.1f}% | "
+            f"{avg_full.get(strategy, 0):.4f} | {avg_overlap.get(strategy, 0):.4f} |"
+        )
+
+    lines.extend(
+        [
+            "",
+            "*Full Area*: Area under each strategy's curve across all ILAD values it can reach.",
+            "*Overlap Area*: Area only in the ILAD range where all strategies compete (fairest comparison).",
+            "",
+        ]
+    )
+    return lines
+
+
 def _format_full_results(results: list[dict]) -> list[str]:
     """Format full results as collapsible markdown."""
     lines = [
@@ -293,7 +484,6 @@ def _format_full_results(results: list[dict]) -> list[str]:
 def generate_markdown(results: list[dict]) -> str:
     """Generate markdown report content from benchmark results."""
     retention_table = _compute_retention_table(results, retention_pct=0.95)
-    f1_per_dataset, f1_ranking = _compute_f1_scores(results)
     regional_data = _compute_regional_winners(results)
 
     md_lines = [
@@ -317,17 +507,29 @@ def generate_markdown(results: list[dict]) -> str:
         "",
     ]
 
+    pareto_data = _compute_pareto_dominance(results)
+    area_data = _compute_pareto_area(results)
+    multi_metric = _compute_multi_metric_summary(results)
+    md_lines.extend(_format_pareto_summary(pareto_data, area_data))
+    md_lines.extend(_format_multi_metric_table(multi_metric))
     md_lines.extend(_format_regional_analysis(regional_data))
     md_lines.extend(_format_retention_table(retention_table))
-    md_lines.extend(_format_ranking_table(f1_ranking))
-    md_lines.extend(_format_f1_details(f1_per_dataset))
     md_lines.extend(_format_full_results(results))
 
     return "\n".join(md_lines)
 
 
-def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
-    """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
+def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
+    """
+    Generate Pareto frontier plot showing relevance vs diversity tradeoff.
+
+    Args:
+    ----
+        all_data: List of benchmark results.
+        output_path: Path to save the plot.
+        diversity_metric: 'ilad' for average diversity, 'ilmd' for minimum diversity.
+
+    """
     import matplotlib.pyplot as plt
 
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
@@ -344,6 +546,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
         "goodreads-rating": "Goodreads",
     }
 
+    metric_label = "ILAD (Avg Diversity)" if diversity_metric == "ilad" else "ILMD (Min Diversity)"
+
     for ax, dataset in zip(axes, datasets):
         ax.axvspan(0.3, 0.5, alpha=0.1, color="blue", label="_Low")
         ax.axvspan(0.5, 0.7, alpha=0.1, color="green", label="_Moderate")
@@ -355,18 +559,18 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
             strategy_points = sorted(strategy_points, key=lambda x: x["lambda"])
 
             if strategy_points:
-                x_vals = [p["ilad"] for p in strategy_points]
+                x_vals = [p[diversity_metric] for p in strategy_points]
                 y_vals = [p["ndcg"] for p in strategy_points]
                 ax.plot(
                     x_vals, y_vals, "o-", color=colors[strategy], label=strategy.upper(), markersize=7, linewidth=2.5
                 )
 
-        ax.set_xlabel("ILAD (Diversity) →", fontsize=10)
+        ax.set_xlabel(f"{metric_label} →", fontsize=10)
         ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
         ax.set_title(name_map.get(dataset, dataset), fontsize=12, fontweight="bold")
         ax.legend(loc="upper right", fontsize=9, framealpha=0.9)
         ax.grid(True, alpha=0.3, linestyle="--")
-        ax.set_xlim(0.25, 1.05)
+        ax.set_xlim(0.0, 1.05)
 
     first_ax = axes[0]
     y_top = first_ax.get_ylim()[1]
@@ -375,7 +579,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path) -> None:
     first_ax.text(0.8, y_top * 0.95, "High", ha="center", fontsize=8, color="orange", alpha=0.7)
     first_ax.text(0.95, y_top * 0.95, "Max", ha="center", fontsize=8, color="red", alpha=0.7)
 
-    plt.suptitle("Relevance vs Diversity Tradeoff by Strategy", fontsize=14, fontweight="bold", y=1.02)
+    title_suffix = "(Average Diversity)" if diversity_metric == "ilad" else "(Minimum Diversity)"
+    plt.suptitle(f"Relevance vs Diversity Tradeoff {title_suffix}", fontsize=14, fontweight="bold", y=1.02)
     plt.tight_layout()
     fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white")
     plt.close()
@@ -449,6 +654,7 @@ def generate_report(results_dir: Path) -> None:
                     "ndcg": run["ndcg@10"],
                     "mrr": run["mrr"],
                     "ilad": run["ilad"],
+                    "ilmd": run["ilmd"],
                 }
             )
 
@@ -457,9 +663,15 @@ def generate_report(results_dir: Path) -> None:
     report_path.write_text(md_content)
     logger.debug(f"Saved: {report_path}")
 
-    pareto_path = results_dir / "pareto.png"
-    generate_pareto_plot(all_data, pareto_path)
-    logger.debug(f"Saved: {pareto_path}")
+    # Generate ILAD plot (average diversity)
+    pareto_ilad_path = results_dir / "pareto_ilad.png"
+    generate_pareto_plot(all_data, pareto_ilad_path, diversity_metric="ilad")
+    logger.debug(f"Saved: {pareto_ilad_path}")
+
+    # Generate ILMD plot (minimum diversity)
+    pareto_ilmd_path = results_dir / "pareto_ilmd.png"
+    generate_pareto_plot(all_data, pareto_ilmd_path, diversity_metric="ilmd")
+    logger.debug(f"Saved: {pareto_ilmd_path}")
 
     latency_path = results_dir / "latency.png"
     generate_latency_plot(latency_path)
diff --git a/benchmarks/results/RESULTS.md b/benchmarks/results/RESULTS.md
index 2db7f0d..f4df449 100644
--- a/benchmarks/results/RESULTS.md
+++ b/benchmarks/results/RESULTS.md
@@ -20,25 +20,56 @@ Comparison of diversification strategies across recommendation datasets.
 | **DPP** | Determinantal Point Process | O(k²·n) |
 | **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
 
+## Overall Performance Summary
+
+### Pareto Frontier Analysis
+
+Points on the Pareto frontier represent optimal relevance-diversity tradeoffs.
+
+| Strategy | Frontier Points | Share | Avg Area (Full) | Avg Area (Overlap) |
+|----------|----------------:|------:|----------------:|-------------------:|
+| **MSD** | 31 | 83.8% | 0.0459 | 0.0422 |
+| **MMR** | 0 | 0.0% | 0.0377 | 0.0377 |
+| **DPP** | 5 | 13.5% | 0.0419 | 0.0361 |
+| **SSD** | 1 | 2.7% | 0.0406 | 0.0309 |
+
+*Full Area*: Area under each strategy's curve across all ILAD values it can reach.
+*Overlap Area*: Area only in the ILAD range where all strategies compete (fairest comparison).
+
+### Multi-Metric Comparison
+
+Area under curve for different relevance-diversity metric combinations:
+
+| Metrics | SSD | DPP | MSD | MMR | Best |
+|---------|:---:|:---:|:---:|:---:|:----:|
+| nDCG vs ILAD | 0.0406 | 0.0419 | 0.0459 | 0.0377 | **MSD** |
+| MRR vs ILAD | 0.0347 | 0.0353 | 0.0382 | 0.0325 | **MSD** |
+| nDCG vs ILMD | 0.0569 | 0.0613 | 0.0261 | 0.0648 | **MMR** |
+| MRR vs ILMD | 0.0505 | 0.0532 | 0.0230 | 0.0573 | **MMR** |
+
+*Higher area = better overall tradeoff between that relevance and diversity metric.*
+
 ## Best Strategy by Diversity Level
 
 Which strategy achieves highest relevance (nDCG) at each diversity (ILAD) level?
 
 | Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Maximum (0.9+) |
 |---------|:-------------:|:------------------:|:--------------:|:--------------:|
-| amazon-product-reviews-video-games | - | - | **MMR** | **MSD** |
-| goodreads-rating | **SSD** | **MSD** | **MSD** | **MSD** |
+| amazon-video-games | - | - | **MMR** | **DPP** |
+| goodreads | **MSD** | **DPP** | **MSD** | **MSD** |
 | lastfm | **SSD** | **MSD** | **MSD** | **MSD** |
 | ml-32m | **SSD** | **DPP** | **MSD** | - |
 
-### Summary: Wins per Strategy by Region
+### Summary: Best nDCG per Strategy by Region
 
-| Region | SSD | DPP | MSD | MMR | Recommendation |
-|--------|:---:|:---:|:---:|:---:|----------------|
-| Low (0.3-0.5) | 3 | - | - | - | Use **SSD** for light diversification |
-| Moderate (0.5-0.7) | - | 1 | 2 | - | Use **MSD** or **DPP** for moderate diversity |
-| High (0.7-0.9) | - | - | 3 | 1 | Use **MSD** for heavy diversification |
-| Maximum (0.9+) | - | - | 3 | - | Use **MSD** for maximum diversity |
+For each region, what's the best nDCG each strategy can achieve (averaged across datasets)?
+
+| Region | SSD | DPP | MSD | MMR | Best |
+|--------|:---:|:---:|:---:|:---:|:----:|
+| Low (0.3-0.5) | 0.085 | 0.084 | 0.082 | 0.083 | **SSD** |
+| Moderate (0.5-0.7) | 0.159 | 0.160 | 0.161 | 0.145 | **MSD** |
+| High (0.7-0.9) | 0.112 | 0.112 | 0.123 | 0.113 | **MSD** |
+| Maximum (0.9+) | 0.187 | 0.192 | 0.207 | 0.167 | **MSD** |
 
 ## Diversity at 95% Relevance Retention
 
@@ -46,177 +77,208 @@ How much diversity (ILAD) can each strategy achieve while maintaining ≥95% of
 
 | Dataset | MMR | MSD | DPP | SSD | Winner |
 |---------|:---:|:---:|:---:|:---:|:------:|
-| amazon-product-reviews-video-games | 0.851 | 0.947 | 0.866 | 0.832 | **MSD** |
-| goodreads-rating | 0.427 | 0.724 | 0.673 | 0.462 | **MSD** |
+| amazon-video-games | 0.891 | 0.964 | 0.916 | 0.905 | **MSD** |
+| goodreads | 0.469 | 0.784 | 0.673 | 0.462 | **MSD** |
 | lastfm | 0.485 | 0.837 | 0.733 | 0.523 | **MSD** |
-| ml-32m | 0.389 | 0.536 | 0.582 | 0.415 | **DPP** |
-
-## Overall Strategy Ranking
-
-Ranking by average F1 score (harmonic mean of normalized nDCG and ILAD):
-
-| Rank | Strategy | Avg F1 | Interpretation |
-|:----:|----------|:------:|----------------|
-| 🥇 | **MSD** | 0.790 | Best diversity with acceptable relevance |
-| 🥈 | **DPP** | 0.661 | Good balance of both metrics |
-| 🥉 | **MMR** | 0.397 | Conservative, relevance-focused |
-| 4. | **SSD** | 0.334 | Conservative, relevance-focused |
-
-## Best F1 Score per Dataset
-
-| Dataset | Strategy | λ | nDCG@10 | ILAD | F1 |
-|---------|----------|--:|--------:|-----:|---:|
-| amazon-product-reviews-video-games | MMR | 0.8 | 0.2557 | 0.935 | 0.697 |
-| amazon-product-reviews-video-games | MSD | 0.4 | 0.2852 | 0.947 | 0.834 |
-| amazon-product-reviews-video-games | DPP | 0.8 | 0.2982 | 0.866 | 0.593 |
-| amazon-product-reviews-video-games | SSD | 0.8 | 0.2987 | 0.832 | 0.425 |
-| goodreads-rating | MMR | 0.8 | 0.0251 | 0.553 | 0.476 |
-| goodreads-rating | MSD | 0.6 | 0.0244 | 0.832 | 0.779 |
-| goodreads-rating | DPP | 0.8 | 0.0272 | 0.673 | 0.683 |
-| goodreads-rating | SSD | 0.8 | 0.0288 | 0.462 | 0.310 |
-| lastfm | MMR | 0.8 | 0.1590 | 0.485 | 0.195 |
-| lastfm | MSD | 0.6 | 0.1537 | 0.837 | 0.821 |
-| lastfm | DPP | 0.8 | 0.1506 | 0.733 | 0.681 |
-| lastfm | SSD | 0.8 | 0.1585 | 0.523 | 0.298 |
-| ml-32m | MMR | 0.8 | 0.0606 | 0.389 | 0.219 |
-| ml-32m | MSD | 0.6 | 0.0512 | 0.660 | 0.727 |
-| ml-32m | DPP | 0.8 | 0.0616 | 0.582 | 0.688 |
-| ml-32m | SSD | 0.8 | 0.0634 | 0.415 | 0.303 |
+| ml-32m | 0.484 | 0.600 | 0.667 | 0.596 | **DPP** |
 
 <details>
 <summary>Full Results by Dataset (click to expand)</summary>
 
-### amazon-product-reviews-video-games
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
-|----------|--:|--------:|----:|-----:|--------:|
-| DPP | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.03ms |
-| DPP | 0.2 | 0.2904 | 0.2497 | 0.798 | 0.21ms |
-| DPP | 0.4 | 0.2917 | 0.2506 | 0.806 | 0.20ms |
-| DPP | 0.6 | 0.2937 | 0.2532 | 0.824 | 0.20ms |
-| DPP | 0.8 | 0.2982 | 0.2576 | 0.866 | 0.21ms |
-| DPP | 1.0 | 0.1711 | 0.1696 | 0.973 | 0.20ms |
-| MMR | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.20ms |
-| MMR | 0.2 | 0.2893 | 0.2477 | 0.785 | 0.17ms |
-| MMR | 0.4 | 0.2961 | 0.2546 | 0.806 | 0.16ms |
-| MMR | 0.6 | 0.3037 | 0.2592 | 0.851 | 0.15ms |
-| MMR | 0.8 | 0.2557 | 0.2287 | 0.935 | 0.16ms |
-| MMR | 1.0 | 0.1727 | 0.1722 | 0.972 | 0.16ms |
-| MSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.17ms |
-| MSD | 0.2 | 0.2916 | 0.2467 | 0.881 | 0.17ms |
-| MSD | 0.4 | 0.2852 | 0.2428 | 0.947 | 0.16ms |
-| MSD | 0.6 | 0.2511 | 0.2258 | 0.975 | 0.16ms |
-| MSD | 0.8 | 0.1946 | 0.1864 | 0.986 | 0.17ms |
-| MSD | 1.0 | 0.1648 | 0.1644 | 0.990 | 0.16ms |
-| SSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.01ms |
-| SSD | 0.2 | 0.2814 | 0.2423 | 0.776 | 1.88ms |
-| SSD | 0.4 | 0.2845 | 0.2451 | 0.782 | 1.80ms |
-| SSD | 0.6 | 0.2911 | 0.2501 | 0.793 | 1.72ms |
-| SSD | 0.8 | 0.2987 | 0.2575 | 0.832 | 1.64ms |
-| SSD | 1.0 | 0.1644 | 0.1653 | 0.980 | 1.66ms |
-
-### goodreads-rating
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
-|----------|--:|--------:|----:|-----:|--------:|
-| DPP | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.05ms |
-| DPP | 0.2 | 0.0290 | 0.0236 | 0.414 | 0.39ms |
-| DPP | 0.4 | 0.0286 | 0.0236 | 0.438 | 0.40ms |
-| DPP | 0.6 | 0.0284 | 0.0234 | 0.490 | 0.35ms |
-| DPP | 0.8 | 0.0272 | 0.0221 | 0.673 | 0.35ms |
-| DPP | 1.0 | 0.0111 | 0.0114 | 0.879 | 0.35ms |
-| MMR | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.28ms |
-| MMR | 0.2 | 0.0284 | 0.0236 | 0.367 | 0.24ms |
-| MMR | 0.4 | 0.0288 | 0.0236 | 0.388 | 0.23ms |
-| MMR | 0.6 | 0.0275 | 0.0229 | 0.427 | 0.24ms |
-| MMR | 0.8 | 0.0251 | 0.0211 | 0.553 | 0.23ms |
-| MMR | 1.0 | 0.0127 | 0.0125 | 0.863 | 0.23ms |
-| MSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.24ms |
-| MSD | 0.2 | 0.0288 | 0.0234 | 0.572 | 0.24ms |
-| MSD | 0.4 | 0.0277 | 0.0219 | 0.724 | 0.24ms |
-| MSD | 0.6 | 0.0244 | 0.0198 | 0.832 | 0.24ms |
-| MSD | 0.8 | 0.0182 | 0.0159 | 0.898 | 0.24ms |
-| MSD | 1.0 | 0.0115 | 0.0113 | 0.927 | 0.24ms |
-| SSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.01ms |
-| SSD | 0.2 | 0.0284 | 0.0238 | 0.360 | 3.68ms |
-| SSD | 0.4 | 0.0284 | 0.0237 | 0.369 | 3.61ms |
-| SSD | 0.6 | 0.0293 | 0.0236 | 0.387 | 3.53ms |
-| SSD | 0.8 | 0.0288 | 0.0236 | 0.462 | 3.53ms |
-| SSD | 1.0 | 0.0115 | 0.0116 | 0.888 | 3.55ms |
+### amazon-video-games
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
+|----------|--:|--------:|----:|-----:|-----:|
+| DPP | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
+| DPP | 0.1 | 0.2892 | 0.2492 | 0.795 | 0.225 |
+| DPP | 0.2 | 0.2904 | 0.2497 | 0.798 | 0.228 |
+| DPP | 0.3 | 0.2914 | 0.2500 | 0.802 | 0.235 |
+| DPP | 0.4 | 0.2917 | 0.2506 | 0.806 | 0.245 |
+| DPP | 0.5 | 0.2918 | 0.2518 | 0.813 | 0.257 |
+| DPP | 0.6 | 0.2937 | 0.2532 | 0.824 | 0.274 |
+| DPP | 0.7 | 0.2965 | 0.2558 | 0.839 | 0.305 |
+| DPP | 0.8 | 0.2982 | 0.2576 | 0.866 | 0.356 |
+| DPP | 0.9 | 0.3060 | 0.2584 | 0.916 | 0.474 |
+| DPP | 1.0 | 0.1711 | 0.1696 | 0.973 | 0.732 |
+| MMR | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
+| MMR | 0.1 | 0.2846 | 0.2442 | 0.778 | 0.190 |
+| MMR | 0.2 | 0.2893 | 0.2477 | 0.785 | 0.204 |
+| MMR | 0.3 | 0.2937 | 0.2513 | 0.793 | 0.219 |
+| MMR | 0.4 | 0.2961 | 0.2546 | 0.806 | 0.239 |
+| MMR | 0.5 | 0.2992 | 0.2564 | 0.823 | 0.272 |
+| MMR | 0.6 | 0.3037 | 0.2592 | 0.851 | 0.327 |
+| MMR | 0.7 | 0.2890 | 0.2498 | 0.891 | 0.418 |
+| MMR | 0.8 | 0.2557 | 0.2287 | 0.935 | 0.552 |
+| MMR | 0.9 | 0.2064 | 0.1978 | 0.963 | 0.712 |
+| MMR | 1.0 | 0.1727 | 0.1722 | 0.972 | 0.782 |
+| MSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
+| MSD | 0.1 | 0.2901 | 0.2461 | 0.831 | 0.209 |
+| MSD | 0.2 | 0.2916 | 0.2467 | 0.881 | 0.243 |
+| MSD | 0.3 | 0.2878 | 0.2448 | 0.921 | 0.281 |
+| MSD | 0.4 | 0.2852 | 0.2428 | 0.947 | 0.324 |
+| MSD | 0.5 | 0.2690 | 0.2345 | 0.964 | 0.368 |
+| MSD | 0.6 | 0.2511 | 0.2258 | 0.975 | 0.401 |
+| MSD | 0.7 | 0.2139 | 0.1999 | 0.982 | 0.437 |
+| MSD | 0.8 | 0.1946 | 0.1864 | 0.986 | 0.466 |
+| MSD | 0.9 | 0.1697 | 0.1674 | 0.989 | 0.489 |
+| MSD | 1.0 | 0.1648 | 0.1644 | 0.990 | 0.491 |
+| SSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
+| SSD | 0.1 | 0.2799 | 0.2414 | 0.775 | 0.183 |
+| SSD | 0.2 | 0.2814 | 0.2423 | 0.776 | 0.187 |
+| SSD | 0.3 | 0.2836 | 0.2436 | 0.779 | 0.188 |
+| SSD | 0.4 | 0.2845 | 0.2451 | 0.782 | 0.194 |
+| SSD | 0.5 | 0.2888 | 0.2477 | 0.786 | 0.201 |
+| SSD | 0.6 | 0.2911 | 0.2501 | 0.793 | 0.211 |
+| SSD | 0.7 | 0.2950 | 0.2536 | 0.806 | 0.233 |
+| SSD | 0.8 | 0.2987 | 0.2575 | 0.832 | 0.271 |
+| SSD | 0.9 | 0.2971 | 0.2539 | 0.905 | 0.406 |
+| SSD | 1.0 | 0.1644 | 0.1653 | 0.980 | 0.729 |
+
+### goodreads
+
+| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
+|----------|--:|--------:|----:|-----:|-----:|
+| DPP | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
+| DPP | 0.1 | 0.0288 | 0.0237 | 0.408 | 0.070 |
+| DPP | 0.2 | 0.0290 | 0.0236 | 0.414 | 0.072 |
+| DPP | 0.3 | 0.0290 | 0.0236 | 0.424 | 0.075 |
+| DPP | 0.4 | 0.0286 | 0.0236 | 0.438 | 0.078 |
+| DPP | 0.5 | 0.0281 | 0.0235 | 0.459 | 0.083 |
+| DPP | 0.6 | 0.0284 | 0.0234 | 0.490 | 0.091 |
+| DPP | 0.7 | 0.0289 | 0.0230 | 0.550 | 0.106 |
+| DPP | 0.8 | 0.0272 | 0.0221 | 0.673 | 0.139 |
+| DPP | 0.9 | 0.0235 | 0.0193 | 0.821 | 0.261 |
+| DPP | 1.0 | 0.0111 | 0.0114 | 0.879 | 0.494 |
+| MMR | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
+| MMR | 0.1 | 0.0281 | 0.0237 | 0.361 | 0.054 |
+| MMR | 0.2 | 0.0284 | 0.0236 | 0.367 | 0.055 |
+| MMR | 0.3 | 0.0288 | 0.0237 | 0.376 | 0.057 |
+| MMR | 0.4 | 0.0288 | 0.0236 | 0.388 | 0.061 |
+| MMR | 0.5 | 0.0279 | 0.0232 | 0.403 | 0.068 |
+| MMR | 0.6 | 0.0275 | 0.0229 | 0.427 | 0.076 |
+| MMR | 0.7 | 0.0274 | 0.0226 | 0.469 | 0.091 |
+| MMR | 0.8 | 0.0251 | 0.0211 | 0.553 | 0.134 |
+| MMR | 0.9 | 0.0198 | 0.0176 | 0.703 | 0.269 |
+| MMR | 1.0 | 0.0127 | 0.0125 | 0.863 | 0.583 |
+| MSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
+| MSD | 0.1 | 0.0294 | 0.0237 | 0.473 | 0.054 |
+| MSD | 0.2 | 0.0288 | 0.0234 | 0.572 | 0.058 |
+| MSD | 0.3 | 0.0283 | 0.0229 | 0.654 | 0.063 |
+| MSD | 0.4 | 0.0277 | 0.0219 | 0.724 | 0.070 |
+| MSD | 0.5 | 0.0269 | 0.0214 | 0.784 | 0.076 |
+| MSD | 0.6 | 0.0244 | 0.0198 | 0.832 | 0.084 |
+| MSD | 0.7 | 0.0212 | 0.0179 | 0.870 | 0.093 |
+| MSD | 0.8 | 0.0182 | 0.0159 | 0.898 | 0.102 |
+| MSD | 0.9 | 0.0151 | 0.0141 | 0.917 | 0.114 |
+| MSD | 1.0 | 0.0115 | 0.0113 | 0.927 | 0.135 |
+| SSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
+| SSD | 0.1 | 0.0281 | 0.0237 | 0.358 | 0.053 |
+| SSD | 0.2 | 0.0284 | 0.0238 | 0.360 | 0.054 |
+| SSD | 0.3 | 0.0286 | 0.0239 | 0.364 | 0.055 |
+| SSD | 0.4 | 0.0284 | 0.0237 | 0.369 | 0.056 |
+| SSD | 0.5 | 0.0289 | 0.0236 | 0.376 | 0.058 |
+| SSD | 0.6 | 0.0293 | 0.0236 | 0.387 | 0.061 |
+| SSD | 0.7 | 0.0294 | 0.0237 | 0.408 | 0.064 |
+| SSD | 0.8 | 0.0288 | 0.0236 | 0.462 | 0.075 |
+| SSD | 0.9 | 0.0232 | 0.0198 | 0.717 | 0.124 |
+| SSD | 1.0 | 0.0115 | 0.0116 | 0.888 | 0.495 |
 
 ### lastfm
 
-| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
-|----------|--:|--------:|----:|-----:|--------:|
-| DPP | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.05ms |
-| DPP | 0.2 | 0.1592 | 0.1326 | 0.471 | 0.40ms |
-| DPP | 0.4 | 0.1593 | 0.1329 | 0.490 | 0.41ms |
-| DPP | 0.6 | 0.1568 | 0.1320 | 0.542 | 0.36ms |
-| DPP | 0.8 | 0.1506 | 0.1258 | 0.733 | 0.35ms |
-| DPP | 1.0 | 0.0784 | 0.0782 | 0.947 | 0.35ms |
-| MMR | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.28ms |
-| MMR | 0.2 | 0.1591 | 0.1319 | 0.429 | 0.24ms |
-| MMR | 0.4 | 0.1597 | 0.1326 | 0.433 | 0.23ms |
-| MMR | 0.6 | 0.1605 | 0.1334 | 0.443 | 0.23ms |
-| MMR | 0.8 | 0.1590 | 0.1332 | 0.485 | 0.23ms |
-| MMR | 1.0 | 0.0780 | 0.0781 | 0.942 | 0.24ms |
-| MSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.25ms |
-| MSD | 0.2 | 0.1606 | 0.1330 | 0.506 | 0.24ms |
-| MSD | 0.4 | 0.1583 | 0.1317 | 0.638 | 0.24ms |
-| MSD | 0.6 | 0.1537 | 0.1261 | 0.837 | 0.24ms |
-| MSD | 0.8 | 0.1270 | 0.1118 | 0.940 | 0.25ms |
-| MSD | 1.0 | 0.0779 | 0.0777 | 0.977 | 0.24ms |
-| SSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.02ms |
-| SSD | 0.2 | 0.1590 | 0.1324 | 0.429 | 3.25ms |
-| SSD | 0.4 | 0.1600 | 0.1329 | 0.435 | 3.20ms |
-| SSD | 0.6 | 0.1608 | 0.1336 | 0.449 | 3.12ms |
-| SSD | 0.8 | 0.1585 | 0.1328 | 0.523 | 3.12ms |
-| SSD | 1.0 | 0.0775 | 0.0776 | 0.958 | 3.09ms |
+| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
+|----------|--:|--------:|----:|-----:|-----:|
+| DPP | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
+| DPP | 0.1 | 0.1598 | 0.1330 | 0.465 | 0.100 |
+| DPP | 0.2 | 0.1592 | 0.1326 | 0.471 | 0.102 |
+| DPP | 0.3 | 0.1588 | 0.1328 | 0.479 | 0.104 |
+| DPP | 0.4 | 0.1593 | 0.1329 | 0.490 | 0.108 |
+| DPP | 0.5 | 0.1598 | 0.1337 | 0.509 | 0.114 |
+| DPP | 0.6 | 0.1568 | 0.1320 | 0.542 | 0.122 |
+| DPP | 0.7 | 0.1578 | 0.1316 | 0.610 | 0.140 |
+| DPP | 0.8 | 0.1506 | 0.1258 | 0.733 | 0.186 |
+| DPP | 0.9 | 0.1275 | 0.1114 | 0.885 | 0.347 |
+| DPP | 1.0 | 0.0784 | 0.0782 | 0.947 | 0.652 |
+| MMR | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
+| MMR | 0.1 | 0.1586 | 0.1319 | 0.427 | 0.084 |
+| MMR | 0.2 | 0.1591 | 0.1319 | 0.429 | 0.084 |
+| MMR | 0.3 | 0.1594 | 0.1320 | 0.430 | 0.085 |
+| MMR | 0.4 | 0.1597 | 0.1326 | 0.433 | 0.086 |
+| MMR | 0.5 | 0.1600 | 0.1330 | 0.437 | 0.087 |
+| MMR | 0.6 | 0.1605 | 0.1334 | 0.443 | 0.089 |
+| MMR | 0.7 | 0.1607 | 0.1334 | 0.455 | 0.093 |
+| MMR | 0.8 | 0.1590 | 0.1332 | 0.485 | 0.103 |
+| MMR | 0.9 | 0.1448 | 0.1246 | 0.646 | 0.171 |
+| MMR | 1.0 | 0.0780 | 0.0781 | 0.942 | 0.726 |
+| MSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
+| MSD | 0.1 | 0.1599 | 0.1326 | 0.462 | 0.084 |
+| MSD | 0.2 | 0.1606 | 0.1330 | 0.506 | 0.085 |
+| MSD | 0.3 | 0.1608 | 0.1326 | 0.562 | 0.087 |
+| MSD | 0.4 | 0.1583 | 0.1317 | 0.638 | 0.089 |
+| MSD | 0.5 | 0.1560 | 0.1294 | 0.742 | 0.095 |
+| MSD | 0.6 | 0.1537 | 0.1261 | 0.837 | 0.103 |
+| MSD | 0.7 | 0.1425 | 0.1201 | 0.900 | 0.118 |
+| MSD | 0.8 | 0.1270 | 0.1118 | 0.940 | 0.141 |
+| MSD | 0.9 | 0.1024 | 0.0960 | 0.965 | 0.188 |
+| MSD | 1.0 | 0.0779 | 0.0777 | 0.977 | 0.274 |
+| SSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
+| SSD | 0.1 | 0.1585 | 0.1319 | 0.427 | 0.084 |
+| SSD | 0.2 | 0.1590 | 0.1324 | 0.429 | 0.084 |
+| SSD | 0.3 | 0.1595 | 0.1326 | 0.432 | 0.085 |
+| SSD | 0.4 | 0.1600 | 0.1329 | 0.435 | 0.087 |
+| SSD | 0.5 | 0.1604 | 0.1332 | 0.440 | 0.088 |
+| SSD | 0.6 | 0.1608 | 0.1336 | 0.449 | 0.091 |
+| SSD | 0.7 | 0.1599 | 0.1330 | 0.465 | 0.095 |
+| SSD | 0.8 | 0.1585 | 0.1328 | 0.523 | 0.107 |
+| SSD | 0.9 | 0.1308 | 0.1144 | 0.825 | 0.194 |
+| SSD | 1.0 | 0.0775 | 0.0776 | 0.958 | 0.654 |
 
 ### ml-32m
 
-| Strategy | λ | nDCG@10 | MRR | ILAD | Latency |
-|----------|--:|--------:|----:|-----:|--------:|
-| DPP | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.04ms |
-| DPP | 0.2 | 0.0614 | 0.0465 | 0.394 | 0.42ms |
-| DPP | 0.4 | 0.0620 | 0.0471 | 0.418 | 0.37ms |
-| DPP | 0.6 | 0.0629 | 0.0477 | 0.473 | 0.33ms |
-| DPP | 0.8 | 0.0616 | 0.0462 | 0.582 | 0.32ms |
-| DPP | 1.0 | 0.0151 | 0.0167 | 0.734 | 0.35ms |
-| MMR | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.27ms |
-| MMR | 0.2 | 0.0553 | 0.0427 | 0.333 | 0.26ms |
-| MMR | 0.4 | 0.0566 | 0.0435 | 0.338 | 0.25ms |
-| MMR | 0.6 | 0.0575 | 0.0445 | 0.348 | 0.30ms |
-| MMR | 0.8 | 0.0606 | 0.0445 | 0.389 | 0.23ms |
-| MMR | 1.0 | 0.0156 | 0.0162 | 0.721 | 0.26ms |
-| MSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.23ms |
-| MSD | 0.2 | 0.0567 | 0.0434 | 0.416 | 0.23ms |
-| MSD | 0.4 | 0.0575 | 0.0426 | 0.536 | 0.24ms |
-| MSD | 0.6 | 0.0512 | 0.0392 | 0.660 | 0.24ms |
-| MSD | 0.8 | 0.0411 | 0.0324 | 0.752 | 0.24ms |
-| MSD | 1.0 | 0.0132 | 0.0132 | 0.801 | 0.23ms |
-| SSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.01ms |
-| SSD | 0.2 | 0.0565 | 0.0431 | 0.335 | 3.45ms |
-| SSD | 0.4 | 0.0579 | 0.0445 | 0.341 | 3.38ms |
-| SSD | 0.6 | 0.0612 | 0.0456 | 0.354 | 3.25ms |
-| SSD | 0.8 | 0.0634 | 0.0472 | 0.415 | 3.25ms |
-| SSD | 1.0 | 0.0149 | 0.0166 | 0.736 | 3.21ms |
+| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
+|----------|--:|--------:|----:|-----:|-----:|
+| DPP | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
+| DPP | 0.1 | 0.0611 | 0.0463 | 0.387 | 0.063 |
+| DPP | 0.2 | 0.0614 | 0.0465 | 0.394 | 0.066 |
+| DPP | 0.3 | 0.0615 | 0.0466 | 0.404 | 0.069 |
+| DPP | 0.4 | 0.0620 | 0.0471 | 0.418 | 0.072 |
+| DPP | 0.5 | 0.0627 | 0.0478 | 0.439 | 0.078 |
+| DPP | 0.6 | 0.0629 | 0.0477 | 0.473 | 0.088 |
+| DPP | 0.7 | 0.0648 | 0.0484 | 0.519 | 0.104 |
+| DPP | 0.8 | 0.0616 | 0.0462 | 0.582 | 0.140 |
+| DPP | 0.9 | 0.0521 | 0.0398 | 0.667 | 0.251 |
+| DPP | 1.0 | 0.0151 | 0.0167 | 0.734 | 0.406 |
+| MMR | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
+| MMR | 0.1 | 0.0551 | 0.0423 | 0.332 | 0.045 |
+| MMR | 0.2 | 0.0553 | 0.0427 | 0.333 | 0.046 |
+| MMR | 0.3 | 0.0560 | 0.0429 | 0.335 | 0.046 |
+| MMR | 0.4 | 0.0566 | 0.0435 | 0.338 | 0.047 |
+| MMR | 0.5 | 0.0569 | 0.0442 | 0.341 | 0.048 |
+| MMR | 0.6 | 0.0575 | 0.0445 | 0.348 | 0.051 |
+| MMR | 0.7 | 0.0599 | 0.0446 | 0.360 | 0.055 |
+| MMR | 0.8 | 0.0606 | 0.0445 | 0.389 | 0.063 |
+| MMR | 0.9 | 0.0567 | 0.0420 | 0.484 | 0.102 |
+| MMR | 1.0 | 0.0156 | 0.0162 | 0.721 | 0.467 |
+| MSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
+| MSD | 0.1 | 0.0555 | 0.0428 | 0.368 | 0.045 |
+| MSD | 0.2 | 0.0567 | 0.0434 | 0.416 | 0.045 |
+| MSD | 0.3 | 0.0581 | 0.0429 | 0.472 | 0.046 |
+| MSD | 0.4 | 0.0575 | 0.0426 | 0.536 | 0.047 |
+| MSD | 0.5 | 0.0553 | 0.0417 | 0.600 | 0.046 |
+| MSD | 0.6 | 0.0512 | 0.0392 | 0.660 | 0.046 |
+| MSD | 0.7 | 0.0483 | 0.0368 | 0.711 | 0.047 |
+| MSD | 0.8 | 0.0411 | 0.0324 | 0.752 | 0.047 |
+| MSD | 0.9 | 0.0258 | 0.0215 | 0.783 | 0.048 |
+| MSD | 1.0 | 0.0132 | 0.0132 | 0.801 | 0.050 |
+| SSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
+| SSD | 0.1 | 0.0558 | 0.0428 | 0.333 | 0.045 |
+| SSD | 0.2 | 0.0565 | 0.0431 | 0.335 | 0.046 |
+| SSD | 0.3 | 0.0574 | 0.0438 | 0.337 | 0.046 |
+| SSD | 0.4 | 0.0579 | 0.0445 | 0.341 | 0.048 |
+| SSD | 0.5 | 0.0594 | 0.0449 | 0.346 | 0.049 |
+| SSD | 0.6 | 0.0612 | 0.0456 | 0.354 | 0.051 |
+| SSD | 0.7 | 0.0607 | 0.0462 | 0.371 | 0.055 |
+| SSD | 0.8 | 0.0634 | 0.0472 | 0.415 | 0.064 |
+| SSD | 0.9 | 0.0537 | 0.0419 | 0.596 | 0.112 |
+| SSD | 1.0 | 0.0149 | 0.0166 | 0.736 | 0.405 |
 
 </details>
-
-## Key Insights
-
-1. **SSD excels at light diversification** - Best nDCG when ILAD is 0.3-0.5
-2. **MSD dominates high diversity regions** - Best choice when ILAD > 0.7
-3. **DPP is a good middle-ground** - Competitive across multiple regions
-4. **MMR is fastest but least effective** - Rarely wins any diversity region
-
-### Choosing the Right Strategy
-
-| Your Goal | Best Strategy | λ | Why |
-|-----------|---------------|---|-----|
-| Light diversification | **SSD** | 0.6-0.8 | Highest nDCG at ILAD 0.3-0.5 |
-| Moderate diversification | **DPP** or **MSD** | 0.4-0.6 | Good balance at ILAD 0.5-0.7 |
-| Heavy diversification | **MSD** | 0.4-0.6 | Best at ILAD 0.7-0.9 |
-| Maximum diversity | **MSD** | 0.6-0.8 | Dominates at ILAD > 0.9 |
-| Speed-critical | **MMR** | 0.6-0.8 | O(k·n) complexity |
diff --git a/benchmarks/results/amazon-product-reviews-video-games.json b/benchmarks/results/amazon-product-reviews-video-games.json
deleted file mode 100644
index cfe8bab..0000000
--- a/benchmarks/results/amazon-product-reviews-video-games.json
+++ /dev/null
@@ -1,351 +0,0 @@
-{
-  "dataset": "amazon-product-reviews-video-games",
-  "timestamp": "2026-01-22T14:27:20.887176+00:00",
-  "config": {
-    "n_users": 707,
-    "n_items": 537,
-    "n_interactions": 5834,
-    "sample_users": 707,
-    "k": 20,
-    "embedding_dim": 64,
-    "seed": 42
-  },
-  "results": [
-    {
-      "strategy": "dpp",
-      "diversity": 0.0,
-      "mrr": 0.24030748494798834,
-      "mrr_std": 0.35792092111973967,
-      "ndcg@10": 0.2790212189683253,
-      "ndcg@10_std": 0.3720897481865557,
-      "ilad": 0.7735240327502578,
-      "ilad_std": 0.08175771190159127,
-      "ilmd": 0.18095086495980853,
-      "ilmd_std": 0.07534733499987091,
-      "latency_ms": 0.028420680072790874,
-      "latency_ms_std": 0.03997715083763415
-    },
-    {
-      "strategy": "dpp",
-      "diversity": 0.2,
-      "mrr": 0.249695691096587,
-      "mrr_std": 0.3576023080438181,
-      "ndcg@10": 0.2904184030580189,
-      "ndcg@10_std": 0.3727431536021708,
-      "ilad": 0.7978060674633636,
-      "ilad_std": 0.07251291136105421,
-      "ilmd": 0.22800128522317176,
-      "ilmd_std": 0.08378991507768188,
-      "latency_ms": 0.20679205224969158,
-      "latency_ms_std": 0.10557277379005317
-    },
-    {
-      "strategy": "dpp",
-      "diversity": 0.4,
-      "mrr": 0.250642548173486,
-      "mrr_std": 0.35734181587645153,
-      "ndcg@10": 0.29174199036871673,
-      "ndcg@10_std": 0.3725686105704199,
-      "ilad": 0.806259483731282,
-      "ilad_std": 0.06944610368057494,
-      "ilmd": 0.24522726387417806,
-      "ilmd_std": 0.0877306977132351,
-      "latency_ms": 0.2032714310369574,
-      "latency_ms_std": 0.1481942113004484
-    },
-    {
-      "strategy": "dpp",
-      "diversity": 0.6,
-      "mrr": 0.25319042883578863,
-      "mrr_std": 0.35707058390923413,
-      "ndcg@10": 0.2937245596859424,
-      "ndcg@10_std": 0.3730378239403123,
-      "ilad": 0.8239857485304459,
-      "ilad_std": 0.060111607438159416,
-      "ilmd": 0.2744749798801697,
-      "ilmd_std": 0.08621843224743451,
-      "latency_ms": 0.2011140759723443,
-      "latency_ms_std": 0.12908097383340897
-    },
-    {
-      "strategy": "dpp",
-      "diversity": 0.8,
-      "mrr": 0.25762424277188184,
-      "mrr_std": 0.3568986007236586,
-      "ndcg@10": 0.29818124085336956,
-      "ndcg@10_std": 0.3737843506888151,
-      "ilad": 0.8662365226462336,
-      "ilad_std": 0.03662034446824663,
-      "ilmd": 0.3563969723743293,
-      "ilmd_std": 0.07805803150165207,
-      "latency_ms": 0.21318258143647914,
-      "latency_ms_std": 0.21065213773617675
-    },
-    {
-      "strategy": "dpp",
-      "diversity": 1.0,
-      "mrr": 0.16958307183154026,
-      "mrr_std": 0.3683318480091218,
-      "ndcg@10": 0.17105421732221834,
-      "ndcg@10_std": 0.37046310708445435,
-      "ilad": 0.9728583699405784,
-      "ilad_std": 0.009008632300109454,
-      "ilmd": 0.7317706014344514,
-      "ilmd_std": 0.05140659506698124,
-      "latency_ms": 0.20020142373137567,
-      "latency_ms_std": 0.1762825946390643
-    },
-    {
-      "strategy": "mmr",
-      "diversity": 0.0,
-      "mrr": 0.24030748494798834,
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-      "latency_ms": 0.19650739135847675,
-      "latency_ms_std": 0.3023844188657512
-    },
-    {
-      "strategy": "mmr",
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-      "latency_ms_std": 0.18653713293185492
-    },
-    {
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-    },
-    {
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-    },
-    {
-      "strategy": "mmr",
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-      "ndcg@10": 0.25565789971508535,
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-      "latency_ms_std": 0.12051430493779025
-    },
-    {
-      "strategy": "mmr",
-      "diversity": 1.0,
-      "mrr": 0.17216500714936306,
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-    },
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-    }
-  ]
-}
diff --git a/benchmarks/results/amazon-video-games.json b/benchmarks/results/amazon-video-games.json
new file mode 100644
index 0000000..1c1c46d
--- /dev/null
+++ b/benchmarks/results/amazon-video-games.json
@@ -0,0 +1,543 @@
+{
+  "dataset": "amazon-video-games",
+  "timestamp": "2026-01-23T11:37:22.939387+00:00",
+  "config": {
+    "n_users": 707,
+    "n_items": 537,
+    "n_interactions": 5834,
+    "sample_users": 707,
+    "k": 20,
+    "embedding_dim": 64,
+    "seed": 42
+  },
+  "results": [
+    {
+      "strategy": "dpp",
+      "diversity": 0.0,
+      "mrr": 0.24030748494798834,
+      "mrr_std": 0.35792092111973967,
+      "ndcg@10": 0.2790212189683253,
+      "ndcg@10_std": 0.3720897481865557,
+      "ilad": 0.7735240327502578,
+      "ilad_std": 0.08175771190159127,
+      "ilmd": 0.18095086495980853,
+      "ilmd_std": 0.07534733499987091
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.1,
+      "mrr": 0.24917049755726237,
+      "mrr_std": 0.35761142668667983,
+      "ndcg@10": 0.2891892107146568,
+      "ndcg@10_std": 0.37291312150543193,
+      "ilad": 0.795186045820676,
+      "ilad_std": 0.07433865721897356,
+      "ilmd": 0.22485715117110558,
+      "ilmd_std": 0.08349544004673304
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.2,
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+      "mrr_std": 0.3576023080438181,
+      "ndcg@10": 0.2904184030580189,
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+      "ilad": 0.7978060674633636,
+      "ilad_std": 0.07251291136105421,
+      "ilmd": 0.22800128522317176,
+      "ilmd_std": 0.08378991507768188
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.3,
+      "mrr": 0.2499704263661879,
+      "mrr_std": 0.3575318832946669,
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+      "ilad": 0.8016368805350474,
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+      "ilmd": 0.23507585796979394,
+      "ilmd_std": 0.08612844153640908
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.4,
+      "mrr": 0.250642548173486,
+      "mrr_std": 0.35734181587645153,
+      "ndcg@10": 0.29174199036871673,
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+      "ilad": 0.806259483731282,
+      "ilad_std": 0.06944610368057494,
+      "ilmd": 0.24522726387417806,
+      "ilmd_std": 0.0877306977132351
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.5,
+      "mrr": 0.25184557934337887,
+      "mrr_std": 0.3573666439346884,
+      "ndcg@10": 0.2918043722695043,
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+      "ilad": 0.8134623875543792,
+      "ilad_std": 0.06558605234260553,
+      "ilmd": 0.2572980931995613,
+      "ilmd_std": 0.08634537602520746
+    },
+    {
+      "strategy": "dpp",
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+}
diff --git a/benchmarks/results/goodreads-rating.json b/benchmarks/results/goodreads-rating.json
deleted file mode 100644
index d0c40c6..0000000
--- a/benchmarks/results/goodreads-rating.json
+++ /dev/null
@@ -1,351 +0,0 @@
-{
-  "dataset": "goodreads-rating",
-  "timestamp": "2026-01-22T14:28:22.491202+00:00",
-  "config": {
-    "n_users": 10279,
-    "n_items": 20980,
-    "n_interactions": 561514,
-    "sample_users": 2000,
-    "k": 20,
-    "embedding_dim": 64,
-    "seed": 42
-  },
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diff --git a/benchmarks/results/goodreads.json b/benchmarks/results/goodreads.json
new file mode 100644
index 0000000..bc0914d
--- /dev/null
+++ b/benchmarks/results/goodreads.json
@@ -0,0 +1,543 @@
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+}
diff --git a/benchmarks/results/lastfm.json b/benchmarks/results/lastfm.json
index e0b62f7..cd7d0a7 100644
--- a/benchmarks/results/lastfm.json
+++ b/benchmarks/results/lastfm.json
@@ -1,6 +1,6 @@
 {
   "dataset": "lastfm",
-  "timestamp": "2026-01-22T14:27:08.191975+00:00",
+  "timestamp": "2026-01-23T11:42:19.508300+00:00",
   "config": {
     "n_users": 1859,
     "n_items": 2823,
@@ -21,9 +21,19 @@
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+      "ilmd_std": 0.0441391250843951
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@@ -35,9 +45,19 @@
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+      "ilmd_std": 0.046223269962524745
+    },
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     },
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@@ -49,9 +69,19 @@
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+      "ilmd_std": 0.046531430876829885
+    },
+    {
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@@ -63,9 +93,19 @@
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+      "ilmd_std": 0.04841453898407571
+    },
+    {
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     },
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@@ -77,9 +117,19 @@
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+      "ilmd_std": 0.04900775963184113
+    },
+    {
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     },
     {
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@@ -91,9 +141,7 @@
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-      "latency_ms_std": 0.24029593408052605
+      "ilmd_std": 0.08798215921254149
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@@ -105,9 +153,19 @@
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+      "ilmd_std": 0.0441391250843951
+    },
+    {
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     {
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@@ -119,9 +177,19 @@
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-      "latency_ms": 0.237470675453207,
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+      "ilmd_std": 0.04446376126322011
+    },
+    {
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     {
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@@ -133,9 +201,19 @@
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@@ -273,9 +417,19 @@
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   ]
 }
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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diff --git a/benchmarks/results/ml-32m.json b/benchmarks/results/ml-32m.json
index 4088ba2..586f326 100644
--- a/benchmarks/results/ml-32m.json
+++ b/benchmarks/results/ml-32m.json
@@ -1,6 +1,6 @@
 {
   "dataset": "ml-32m",
-  "timestamp": "2026-01-22T14:26:24.554531+00:00",
+  "timestamp": "2026-01-23T11:41:09.158730+00:00",
   "config": {
     "n_users": 198973,
     "n_items": 25051,
@@ -21,9 +21,19 @@
       "ilad": 0.33115221637580544,
       "ilad_std": 0.11218300237386442,
       "ilmd": 0.044682448506355284,
-      "ilmd_std": 0.02984058564906046,
-      "latency_ms": 0.04408438570681028,
-      "latency_ms_std": 0.028718946501992575
+      "ilmd_std": 0.02984058564906046
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.1,
+      "mrr": 0.04625330080780932,
+      "mrr_std": 0.14197817730321166,
+      "ndcg@10": 0.06107098204808578,
+      "ndcg@10_std": 0.17618024591263826,
+      "ilad": 0.38657995719090105,
+      "ilad_std": 0.1014751843107147,
+      "ilmd": 0.06317317193746566,
+      "ilmd_std": 0.035930322525339634
     },
     {
       "strategy": "dpp",
@@ -35,9 +45,19 @@
       "ilad": 0.3944173552542925,
       "ilad_std": 0.09937103785809154,
       "ilmd": 0.06576073378324508,
-      "ilmd_std": 0.036840706366985936,
-      "latency_ms": 0.41815552400657907,
-      "latency_ms_std": 0.385945568022237
+      "ilmd_std": 0.036840706366985936
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.3,
+      "mrr": 0.0466205170882609,
+      "mrr_std": 0.14219360514801124,
+      "ndcg@10": 0.06145262661661947,
+      "ndcg@10_std": 0.1766456102618773,
+      "ilad": 0.4039573872499168,
+      "ilad_std": 0.09669730781516228,
+      "ilmd": 0.06866682934761047,
+      "ilmd_std": 0.03773565714350411
     },
     {
       "strategy": "dpp",
@@ -49,9 +69,19 @@
       "ilad": 0.4176843299381435,
       "ilad_std": 0.0904572616578133,
       "ilmd": 0.07244103068113326,
-      "ilmd_std": 0.038370315513166144,
-      "latency_ms": 0.36669555652770214,
-      "latency_ms_std": 0.49013741081951134
+      "ilmd_std": 0.038370315513166144
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.5,
+      "mrr": 0.04783221942745936,
+      "mrr_std": 0.1427981063914046,
+      "ndcg@10": 0.06271219555682002,
+      "ndcg@10_std": 0.1778509428735068,
+      "ilad": 0.43899736285209656,
+      "ilad_std": 0.0790901970978327,
+      "ilmd": 0.0783798314332962,
+      "ilmd_std": 0.038775876137963405
     },
     {
       "strategy": "dpp",
@@ -63,9 +93,19 @@
       "ilad": 0.47282045040279624,
       "ilad_std": 0.06279411768782571,
       "ilmd": 0.08764451596140861,
-      "ilmd_std": 0.03911897694939485,
-      "latency_ms": 0.3252962980477605,
-      "latency_ms_std": 0.1917037420330699
+      "ilmd_std": 0.03911897694939485
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.7,
+      "mrr": 0.04841624516479392,
+      "mrr_std": 0.14209593166145468,
+      "ndcg@10": 0.06483465175040233,
+      "ndcg@10_std": 0.1784302201661079,
+      "ilad": 0.5188692592531443,
+      "ilad_std": 0.05505707064582957,
+      "ilmd": 0.1036525419652462,
+      "ilmd_std": 0.040394514377814
     },
     {
       "strategy": "dpp",
@@ -77,9 +117,19 @@
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       "ilad_std": 0.054591632786161484,
       "ilmd": 0.139842008292675,
-      "ilmd_std": 0.0452118150089321,
-      "latency_ms": 0.32093744195299223,
-      "latency_ms_std": 0.25686056584118744
+      "ilmd_std": 0.0452118150089321
+    },
+    {
+      "strategy": "dpp",
+      "diversity": 0.9,
+      "mrr": 0.03983566189142815,
+      "mrr_std": 0.13613349012037756,
+      "ndcg@10": 0.05206140533740895,
+      "ndcg@10_std": 0.1661201739680596,
+      "ilad": 0.6671502260118722,
+      "ilad_std": 0.040196609249957736,
+      "ilmd": 0.25135905304551126,
+      "ilmd_std": 0.05873323240193932
     },
     {
       "strategy": "dpp",
@@ -91,9 +141,7 @@
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       "ilad_std": 0.04415610929143557,
       "ilmd": 0.40633358478546144,
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diff --git a/benchmarks/results/pareto.png b/benchmarks/results/pareto.png
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HcmV?d00001


From 2ca59a502391e39ede76003c60209e22fa7b352e Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 13:07:09 +0100
Subject: [PATCH 08/69] Updated benchmarks

---
 benchmarks/README.md           |  36 ++---
 benchmarks/core/report.py      |  65 +++++---
 benchmarks/results/RESULTS.md  | 284 ---------------------------------
 benchmarks/results/latency.png | Bin 75566 -> 76195 bytes
 4 files changed, 59 insertions(+), 326 deletions(-)
 delete mode 100644 benchmarks/results/RESULTS.md

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 8b769cb..d9c15bf 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -4,7 +4,7 @@ This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**,
 
 For each dataset, we use a leave-one-out evaluation protocol (meaning we hold out one item
 per user as the test set), generate candidate items based on similar items, then rerank with each
-diversification strategy. We measure relevance (nDCG, MRR) and diversity (ILAD, ILMD) to trace the
+diversification strategy. We measure relevance (nDCG) and diversity (ILAD, ILMD) to trace the
 Pareto frontier of the relevance-diversity tradeoff.
 
 In addition, we measure latency of each strategy as the number of candidates scales.
@@ -29,31 +29,32 @@ We evaluate using two diversity metrics that capture different aspects:
 - **ILAD** (Intra-List Average Distance): Measures overall diversity spread
 - **ILMD** (Intra-List Minimum Distance): Ensures no very similar item pairs (worst-case diversity)
 
-### Summary: Pareto Area Under Curve
+### Pareto Area Under Curve
 
-The Pareto area measures the total relevance-diversity tradeoff quality by computing the area under each strategy's curve. Higher area = better overall performance across all diversity levels.
+The Pareto area measures the relevance-diversity tradeoff quality by computing the area under each strategy's curve in the **diversification region** (diversity ≥ 0.3). Higher = better.
 
-| Metric | Best | 2nd | 3rd | 4th |
-|--------|------|-----|-----|-----|
-| **nDCG vs ILAD** | MSD | DPP | SSD | MMR |
-| **nDCG vs ILMD** | MMR* | DPP | SSD | MSD |
-
-*\*MMR "wins" ILMD by not diversifying—it keeps items similar (high relevance, low diversity). For actual diversification, use DPP or SSD.*
+| Strategy | nDCG vs ILAD | nDCG vs ILMD | Combined | Notes |
+|----------|:------------:|:------------:|:--------:|-------|
+| **DPP**  | 0.042        | 0.036        | **0.039** | Best overall balance |
+| **MSD**  | **0.046**    | 0.010        | 0.028    | Best for ILAD (overall variety) |
+| **SSD**  | 0.041        | 0.019        | 0.030    | Good balance |
+| **MMR**  | 0.038        | 0.029        | 0.033    | Simple baseline |
 
 **Key insight:** The "best" strategy depends on your diversity metric:
-- **MSD** maximizes average pairwise distance—great for overall variety
-- **DPP/SSD** better ensure no very similar pairs slip through
-- **MMR** achieves high ILMD by barely diversifying (not recommended if diversity matters)
+- **DPP** wins on combined score—best overall tradeoff
+- **MSD** dominates ILAD—great for maximum variety, but poor ILMD (allows similar pairs)
+- **SSD/DPP** better ensure no very similar pairs slip through
 
 ### Recommendations
 
 | Goal | Strategy | λ | Why |
 |-----------|----------|---|-----|
-| Maximum relevance, light diversity | **SSD** | 0.7-0.9 | Best nDCG at low ILAD |
-| Balanced tradeoff | **DPP** | 0.4-0.6 | Good on both ILAD and ILMD |
+| Balanced tradeoff | **DPP** | 0.4-0.6 | Highest combined Pareto area |
+| Maximum relevance, light diversity | **SSD** | 0.7-0.9 | Best nDCG at low diversity |
 | High average diversity | **MSD** | 0.3-0.5 | Dominates ILAD metric |
 | No similar pairs (strict) | **DPP** | 0.3-0.5 | Best ILMD scores |
 | Extreme diversity | **MSD** | 0.1-0.3 | Only strategy reaching ILAD > 0.9 |
+| Simple baseline | **MMR** | 0.5-0.7 | Easy to implement and tune |
 
 ## Relevance-Diversity Tradeoff
 
@@ -120,11 +121,10 @@ python -m benchmarks report
 | Metric | Type | Description |
 |--------|------|-------------|
 | nDCG@k | Relevance | Normalized Discounted Cumulative Gain |
-| MRR | Relevance | Mean Reciprocal Rank |
 | ILAD | Diversity | Intra-List Average Distance (mean pairwise) |
 | ILMD | Diversity | Intra-List Minimum Distance (worst-case pair) |
 
-### Per-Dataset Winners
+### Per-Dataset Winners (by ILAD region)
 
 | Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Max (0.9+) |
 |---------|:-------------:|:------------------:|:--------------:|:----------:|
@@ -133,9 +133,7 @@ python -m benchmarks report
 | Amazon-VG | - | - | MMR | MSD |
 | Goodreads | SSD | MSD | MSD | MSD |
 
-### Full Results
-
-See [`results/RESULTS.md`](results/RESULTS.md) for complete tables.
+*Raw JSON results are in `results/*.json`*
 
 </details>
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index f221093..4b5a552 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -348,32 +348,19 @@ def _compute_pareto_dominance(results: list[dict]) -> dict:
     }
 
 
-def _compute_multi_metric_summary(results: list[dict]) -> dict:
+def _compute_multi_metric_summary(results: list[dict], min_diversity: float = 0.3) -> dict:
     """
-    Compute Pareto area for multiple metric combinations.
+    Compute Pareto area for nDCG vs ILAD and nDCG vs ILMD.
 
-    Analyzes: nDCG vs ILAD, MRR vs ILAD, nDCG vs ILMD, MRR vs ILMD
+    Only counts points in the diversification region (diversity >= min_diversity)
+    to avoid rewarding strategies that simply don't diversify.
     """
     strategies = ["mmr", "msd", "dpp", "ssd"]
     metric_pairs = [
         ("ndcg@10", "ilad", "nDCG vs ILAD"),
-        ("mrr", "ilad", "MRR vs ILAD"),
         ("ndcg@10", "ilmd", "nDCG vs ILMD"),
-        ("mrr", "ilmd", "MRR vs ILMD"),
     ]
 
-    def _compute_area(points: list[dict], x_key: str, y_key: str) -> float:
-        """Compute area under curve using trapezoidal rule."""
-        if len(points) < 2:
-            return 0.0
-        sorted_pts = sorted(points, key=lambda p: p[x_key])
-        area = 0.0
-        for i in range(len(sorted_pts) - 1):
-            dx = sorted_pts[i + 1][x_key] - sorted_pts[i][x_key]
-            avg_y = (sorted_pts[i][y_key] + sorted_pts[i + 1][y_key]) / 2
-            area += dx * avg_y
-        return area
-
     summary: dict[str, dict[str, float]] = {label: {} for _, _, label in metric_pairs}
 
     for rel_key, div_key, label in metric_pairs:
@@ -381,12 +368,13 @@ def _compute_area(points: list[dict], x_key: str, y_key: str) -> float:
 
         for dataset_result in results:
             for s in strategies:
+                # Filter to only include points in the diversification region
                 points = [
                     {rel_key: run[rel_key], div_key: run[div_key]}
                     for run in dataset_result["results"]
-                    if run["strategy"] == s
+                    if run["strategy"] == s and run[div_key] >= min_diversity
                 ]
-                area = _compute_area(points, div_key, rel_key)
+                area = _trapezoidal_area_generic(points, div_key, rel_key)
                 strategy_areas[s].append(area)
 
         # Average across datasets
@@ -396,30 +384,61 @@ def _compute_area(points: list[dict], x_key: str, y_key: str) -> float:
     return summary
 
 
+def _trapezoidal_area_generic(points: list[dict], x_key: str, y_key: str) -> float:
+    """Compute area under curve using trapezoidal rule with configurable keys."""
+    if len(points) < 2:
+        return 0.0
+    sorted_pts = sorted(points, key=lambda p: p[x_key])
+    area = 0.0
+    for i in range(len(sorted_pts) - 1):
+        dx = sorted_pts[i + 1][x_key] - sorted_pts[i][x_key]
+        avg_y = (sorted_pts[i][y_key] + sorted_pts[i + 1][y_key]) / 2
+        area += dx * avg_y
+    return area
+
+
 def _format_multi_metric_table(multi_metric: dict) -> list[str]:
-    """Format multi-metric summary as markdown table."""
+    """Format multi-metric summary as markdown table with combined score."""
     lines = [
         "### Multi-Metric Comparison\n",
-        "Area under curve for different relevance-diversity metric combinations:\n",
+        "Area under Pareto curve (AUC) for different metric combinations:\n",
         "| Metrics | SSD | DPP | MSD | MMR | Best |",
         "|---------|:---:|:---:|:---:|:---:|:----:|",
     ]
 
+    strategies = ["ssd", "dpp", "msd", "mmr"]
+    combined_scores: dict[str, float] = {s: 0.0 for s in strategies}
+
     for label, areas in multi_metric.items():
         row = [label]
         best_s = ""
         best_val = -1.0
-        for s in ["ssd", "dpp", "msd", "mmr"]:
+        for s in strategies:
             val = areas.get(s, 0)
             row.append(f"{val:.4f}")
+            combined_scores[s] += val
             if val > best_val:
                 best_val = val
                 best_s = s.upper()
         row.append(f"**{best_s}**")
         lines.append("| " + " | ".join(row) + " |")
 
+    # Add combined row (average across all metric pairs)
+    num_metrics = len(multi_metric)
+    row = ["**Combined (avg)**"]
+    best_s = ""
+    best_val = -1.0
+    for s in strategies:
+        avg_val = combined_scores[s] / num_metrics if num_metrics > 0 else 0.0
+        row.append(f"**{avg_val:.4f}**")
+        if avg_val > best_val:
+            best_val = avg_val
+            best_s = s.upper()
+    row.append(f"**{best_s}**")
+    lines.append("| " + " | ".join(row) + " |")
+
     lines.append("")
-    lines.append("*Higher area = better overall tradeoff between that relevance and diversity metric.*")
+    lines.append("*Higher AUC = better overall tradeoff. Combined averages across all metric pairs.*")
     lines.append("")
     return lines
 
diff --git a/benchmarks/results/RESULTS.md b/benchmarks/results/RESULTS.md
deleted file mode 100644
index f4df449..0000000
--- a/benchmarks/results/RESULTS.md
+++ /dev/null
@@ -1,284 +0,0 @@
-# Pyversity Benchmark Results
-
-Comparison of diversification strategies across recommendation datasets.
-
-## Datasets
-
-| Dataset | Source | Description |
-|---------|--------|-------------|
-| MovieLens-32M | GroupLens | 32M ratings from MovieLens |
-| Last.FM | HetRec 2011 | Music listening data |
-| Amazon Video Games | McAuley Lab | Product reviews |
-| Goodreads | McAuley Lab | Book ratings |
-
-## Strategies
-
-| Strategy | Description | Complexity |
-|----------|-------------|------------|
-| **MMR** | Maximal Marginal Relevance | O(k·n) |
-| **MSD** | Max-Sum Diversification | O(k·n) |
-| **DPP** | Determinantal Point Process | O(k²·n) |
-| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |
-
-## Overall Performance Summary
-
-### Pareto Frontier Analysis
-
-Points on the Pareto frontier represent optimal relevance-diversity tradeoffs.
-
-| Strategy | Frontier Points | Share | Avg Area (Full) | Avg Area (Overlap) |
-|----------|----------------:|------:|----------------:|-------------------:|
-| **MSD** | 31 | 83.8% | 0.0459 | 0.0422 |
-| **MMR** | 0 | 0.0% | 0.0377 | 0.0377 |
-| **DPP** | 5 | 13.5% | 0.0419 | 0.0361 |
-| **SSD** | 1 | 2.7% | 0.0406 | 0.0309 |
-
-*Full Area*: Area under each strategy's curve across all ILAD values it can reach.
-*Overlap Area*: Area only in the ILAD range where all strategies compete (fairest comparison).
-
-### Multi-Metric Comparison
-
-Area under curve for different relevance-diversity metric combinations:
-
-| Metrics | SSD | DPP | MSD | MMR | Best |
-|---------|:---:|:---:|:---:|:---:|:----:|
-| nDCG vs ILAD | 0.0406 | 0.0419 | 0.0459 | 0.0377 | **MSD** |
-| MRR vs ILAD | 0.0347 | 0.0353 | 0.0382 | 0.0325 | **MSD** |
-| nDCG vs ILMD | 0.0569 | 0.0613 | 0.0261 | 0.0648 | **MMR** |
-| MRR vs ILMD | 0.0505 | 0.0532 | 0.0230 | 0.0573 | **MMR** |
-
-*Higher area = better overall tradeoff between that relevance and diversity metric.*
-
-## Best Strategy by Diversity Level
-
-Which strategy achieves highest relevance (nDCG) at each diversity (ILAD) level?
-
-| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Maximum (0.9+) |
-|---------|:-------------:|:------------------:|:--------------:|:--------------:|
-| amazon-video-games | - | - | **MMR** | **DPP** |
-| goodreads | **MSD** | **DPP** | **MSD** | **MSD** |
-| lastfm | **SSD** | **MSD** | **MSD** | **MSD** |
-| ml-32m | **SSD** | **DPP** | **MSD** | - |
-
-### Summary: Best nDCG per Strategy by Region
-
-For each region, what's the best nDCG each strategy can achieve (averaged across datasets)?
-
-| Region | SSD | DPP | MSD | MMR | Best |
-|--------|:---:|:---:|:---:|:---:|:----:|
-| Low (0.3-0.5) | 0.085 | 0.084 | 0.082 | 0.083 | **SSD** |
-| Moderate (0.5-0.7) | 0.159 | 0.160 | 0.161 | 0.145 | **MSD** |
-| High (0.7-0.9) | 0.112 | 0.112 | 0.123 | 0.113 | **MSD** |
-| Maximum (0.9+) | 0.187 | 0.192 | 0.207 | 0.167 | **MSD** |
-
-## Diversity at 95% Relevance Retention
-
-How much diversity (ILAD) can each strategy achieve while maintaining ≥95% of baseline nDCG?
-
-| Dataset | MMR | MSD | DPP | SSD | Winner |
-|---------|:---:|:---:|:---:|:---:|:------:|
-| amazon-video-games | 0.891 | 0.964 | 0.916 | 0.905 | **MSD** |
-| goodreads | 0.469 | 0.784 | 0.673 | 0.462 | **MSD** |
-| lastfm | 0.485 | 0.837 | 0.733 | 0.523 | **MSD** |
-| ml-32m | 0.484 | 0.600 | 0.667 | 0.596 | **DPP** |
-
-<details>
-<summary>Full Results by Dataset (click to expand)</summary>
-
-### amazon-video-games
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
-|----------|--:|--------:|----:|-----:|-----:|
-| DPP | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
-| DPP | 0.1 | 0.2892 | 0.2492 | 0.795 | 0.225 |
-| DPP | 0.2 | 0.2904 | 0.2497 | 0.798 | 0.228 |
-| DPP | 0.3 | 0.2914 | 0.2500 | 0.802 | 0.235 |
-| DPP | 0.4 | 0.2917 | 0.2506 | 0.806 | 0.245 |
-| DPP | 0.5 | 0.2918 | 0.2518 | 0.813 | 0.257 |
-| DPP | 0.6 | 0.2937 | 0.2532 | 0.824 | 0.274 |
-| DPP | 0.7 | 0.2965 | 0.2558 | 0.839 | 0.305 |
-| DPP | 0.8 | 0.2982 | 0.2576 | 0.866 | 0.356 |
-| DPP | 0.9 | 0.3060 | 0.2584 | 0.916 | 0.474 |
-| DPP | 1.0 | 0.1711 | 0.1696 | 0.973 | 0.732 |
-| MMR | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
-| MMR | 0.1 | 0.2846 | 0.2442 | 0.778 | 0.190 |
-| MMR | 0.2 | 0.2893 | 0.2477 | 0.785 | 0.204 |
-| MMR | 0.3 | 0.2937 | 0.2513 | 0.793 | 0.219 |
-| MMR | 0.4 | 0.2961 | 0.2546 | 0.806 | 0.239 |
-| MMR | 0.5 | 0.2992 | 0.2564 | 0.823 | 0.272 |
-| MMR | 0.6 | 0.3037 | 0.2592 | 0.851 | 0.327 |
-| MMR | 0.7 | 0.2890 | 0.2498 | 0.891 | 0.418 |
-| MMR | 0.8 | 0.2557 | 0.2287 | 0.935 | 0.552 |
-| MMR | 0.9 | 0.2064 | 0.1978 | 0.963 | 0.712 |
-| MMR | 1.0 | 0.1727 | 0.1722 | 0.972 | 0.782 |
-| MSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
-| MSD | 0.1 | 0.2901 | 0.2461 | 0.831 | 0.209 |
-| MSD | 0.2 | 0.2916 | 0.2467 | 0.881 | 0.243 |
-| MSD | 0.3 | 0.2878 | 0.2448 | 0.921 | 0.281 |
-| MSD | 0.4 | 0.2852 | 0.2428 | 0.947 | 0.324 |
-| MSD | 0.5 | 0.2690 | 0.2345 | 0.964 | 0.368 |
-| MSD | 0.6 | 0.2511 | 0.2258 | 0.975 | 0.401 |
-| MSD | 0.7 | 0.2139 | 0.1999 | 0.982 | 0.437 |
-| MSD | 0.8 | 0.1946 | 0.1864 | 0.986 | 0.466 |
-| MSD | 0.9 | 0.1697 | 0.1674 | 0.989 | 0.489 |
-| MSD | 1.0 | 0.1648 | 0.1644 | 0.990 | 0.491 |
-| SSD | 0.0 | 0.2790 | 0.2403 | 0.774 | 0.181 |
-| SSD | 0.1 | 0.2799 | 0.2414 | 0.775 | 0.183 |
-| SSD | 0.2 | 0.2814 | 0.2423 | 0.776 | 0.187 |
-| SSD | 0.3 | 0.2836 | 0.2436 | 0.779 | 0.188 |
-| SSD | 0.4 | 0.2845 | 0.2451 | 0.782 | 0.194 |
-| SSD | 0.5 | 0.2888 | 0.2477 | 0.786 | 0.201 |
-| SSD | 0.6 | 0.2911 | 0.2501 | 0.793 | 0.211 |
-| SSD | 0.7 | 0.2950 | 0.2536 | 0.806 | 0.233 |
-| SSD | 0.8 | 0.2987 | 0.2575 | 0.832 | 0.271 |
-| SSD | 0.9 | 0.2971 | 0.2539 | 0.905 | 0.406 |
-| SSD | 1.0 | 0.1644 | 0.1653 | 0.980 | 0.729 |
-
-### goodreads
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
-|----------|--:|--------:|----:|-----:|-----:|
-| DPP | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
-| DPP | 0.1 | 0.0288 | 0.0237 | 0.408 | 0.070 |
-| DPP | 0.2 | 0.0290 | 0.0236 | 0.414 | 0.072 |
-| DPP | 0.3 | 0.0290 | 0.0236 | 0.424 | 0.075 |
-| DPP | 0.4 | 0.0286 | 0.0236 | 0.438 | 0.078 |
-| DPP | 0.5 | 0.0281 | 0.0235 | 0.459 | 0.083 |
-| DPP | 0.6 | 0.0284 | 0.0234 | 0.490 | 0.091 |
-| DPP | 0.7 | 0.0289 | 0.0230 | 0.550 | 0.106 |
-| DPP | 0.8 | 0.0272 | 0.0221 | 0.673 | 0.139 |
-| DPP | 0.9 | 0.0235 | 0.0193 | 0.821 | 0.261 |
-| DPP | 1.0 | 0.0111 | 0.0114 | 0.879 | 0.494 |
-| MMR | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
-| MMR | 0.1 | 0.0281 | 0.0237 | 0.361 | 0.054 |
-| MMR | 0.2 | 0.0284 | 0.0236 | 0.367 | 0.055 |
-| MMR | 0.3 | 0.0288 | 0.0237 | 0.376 | 0.057 |
-| MMR | 0.4 | 0.0288 | 0.0236 | 0.388 | 0.061 |
-| MMR | 0.5 | 0.0279 | 0.0232 | 0.403 | 0.068 |
-| MMR | 0.6 | 0.0275 | 0.0229 | 0.427 | 0.076 |
-| MMR | 0.7 | 0.0274 | 0.0226 | 0.469 | 0.091 |
-| MMR | 0.8 | 0.0251 | 0.0211 | 0.553 | 0.134 |
-| MMR | 0.9 | 0.0198 | 0.0176 | 0.703 | 0.269 |
-| MMR | 1.0 | 0.0127 | 0.0125 | 0.863 | 0.583 |
-| MSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
-| MSD | 0.1 | 0.0294 | 0.0237 | 0.473 | 0.054 |
-| MSD | 0.2 | 0.0288 | 0.0234 | 0.572 | 0.058 |
-| MSD | 0.3 | 0.0283 | 0.0229 | 0.654 | 0.063 |
-| MSD | 0.4 | 0.0277 | 0.0219 | 0.724 | 0.070 |
-| MSD | 0.5 | 0.0269 | 0.0214 | 0.784 | 0.076 |
-| MSD | 0.6 | 0.0244 | 0.0198 | 0.832 | 0.084 |
-| MSD | 0.7 | 0.0212 | 0.0179 | 0.870 | 0.093 |
-| MSD | 0.8 | 0.0182 | 0.0159 | 0.898 | 0.102 |
-| MSD | 0.9 | 0.0151 | 0.0141 | 0.917 | 0.114 |
-| MSD | 1.0 | 0.0115 | 0.0113 | 0.927 | 0.135 |
-| SSD | 0.0 | 0.0280 | 0.0235 | 0.356 | 0.053 |
-| SSD | 0.1 | 0.0281 | 0.0237 | 0.358 | 0.053 |
-| SSD | 0.2 | 0.0284 | 0.0238 | 0.360 | 0.054 |
-| SSD | 0.3 | 0.0286 | 0.0239 | 0.364 | 0.055 |
-| SSD | 0.4 | 0.0284 | 0.0237 | 0.369 | 0.056 |
-| SSD | 0.5 | 0.0289 | 0.0236 | 0.376 | 0.058 |
-| SSD | 0.6 | 0.0293 | 0.0236 | 0.387 | 0.061 |
-| SSD | 0.7 | 0.0294 | 0.0237 | 0.408 | 0.064 |
-| SSD | 0.8 | 0.0288 | 0.0236 | 0.462 | 0.075 |
-| SSD | 0.9 | 0.0232 | 0.0198 | 0.717 | 0.124 |
-| SSD | 1.0 | 0.0115 | 0.0116 | 0.888 | 0.495 |
-
-### lastfm
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
-|----------|--:|--------:|----:|-----:|-----:|
-| DPP | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
-| DPP | 0.1 | 0.1598 | 0.1330 | 0.465 | 0.100 |
-| DPP | 0.2 | 0.1592 | 0.1326 | 0.471 | 0.102 |
-| DPP | 0.3 | 0.1588 | 0.1328 | 0.479 | 0.104 |
-| DPP | 0.4 | 0.1593 | 0.1329 | 0.490 | 0.108 |
-| DPP | 0.5 | 0.1598 | 0.1337 | 0.509 | 0.114 |
-| DPP | 0.6 | 0.1568 | 0.1320 | 0.542 | 0.122 |
-| DPP | 0.7 | 0.1578 | 0.1316 | 0.610 | 0.140 |
-| DPP | 0.8 | 0.1506 | 0.1258 | 0.733 | 0.186 |
-| DPP | 0.9 | 0.1275 | 0.1114 | 0.885 | 0.347 |
-| DPP | 1.0 | 0.0784 | 0.0782 | 0.947 | 0.652 |
-| MMR | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
-| MMR | 0.1 | 0.1586 | 0.1319 | 0.427 | 0.084 |
-| MMR | 0.2 | 0.1591 | 0.1319 | 0.429 | 0.084 |
-| MMR | 0.3 | 0.1594 | 0.1320 | 0.430 | 0.085 |
-| MMR | 0.4 | 0.1597 | 0.1326 | 0.433 | 0.086 |
-| MMR | 0.5 | 0.1600 | 0.1330 | 0.437 | 0.087 |
-| MMR | 0.6 | 0.1605 | 0.1334 | 0.443 | 0.089 |
-| MMR | 0.7 | 0.1607 | 0.1334 | 0.455 | 0.093 |
-| MMR | 0.8 | 0.1590 | 0.1332 | 0.485 | 0.103 |
-| MMR | 0.9 | 0.1448 | 0.1246 | 0.646 | 0.171 |
-| MMR | 1.0 | 0.0780 | 0.0781 | 0.942 | 0.726 |
-| MSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
-| MSD | 0.1 | 0.1599 | 0.1326 | 0.462 | 0.084 |
-| MSD | 0.2 | 0.1606 | 0.1330 | 0.506 | 0.085 |
-| MSD | 0.3 | 0.1608 | 0.1326 | 0.562 | 0.087 |
-| MSD | 0.4 | 0.1583 | 0.1317 | 0.638 | 0.089 |
-| MSD | 0.5 | 0.1560 | 0.1294 | 0.742 | 0.095 |
-| MSD | 0.6 | 0.1537 | 0.1261 | 0.837 | 0.103 |
-| MSD | 0.7 | 0.1425 | 0.1201 | 0.900 | 0.118 |
-| MSD | 0.8 | 0.1270 | 0.1118 | 0.940 | 0.141 |
-| MSD | 0.9 | 0.1024 | 0.0960 | 0.965 | 0.188 |
-| MSD | 1.0 | 0.0779 | 0.0777 | 0.977 | 0.274 |
-| SSD | 0.0 | 0.1582 | 0.1315 | 0.426 | 0.083 |
-| SSD | 0.1 | 0.1585 | 0.1319 | 0.427 | 0.084 |
-| SSD | 0.2 | 0.1590 | 0.1324 | 0.429 | 0.084 |
-| SSD | 0.3 | 0.1595 | 0.1326 | 0.432 | 0.085 |
-| SSD | 0.4 | 0.1600 | 0.1329 | 0.435 | 0.087 |
-| SSD | 0.5 | 0.1604 | 0.1332 | 0.440 | 0.088 |
-| SSD | 0.6 | 0.1608 | 0.1336 | 0.449 | 0.091 |
-| SSD | 0.7 | 0.1599 | 0.1330 | 0.465 | 0.095 |
-| SSD | 0.8 | 0.1585 | 0.1328 | 0.523 | 0.107 |
-| SSD | 0.9 | 0.1308 | 0.1144 | 0.825 | 0.194 |
-| SSD | 1.0 | 0.0775 | 0.0776 | 0.958 | 0.654 |
-
-### ml-32m
-
-| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |
-|----------|--:|--------:|----:|-----:|-----:|
-| DPP | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
-| DPP | 0.1 | 0.0611 | 0.0463 | 0.387 | 0.063 |
-| DPP | 0.2 | 0.0614 | 0.0465 | 0.394 | 0.066 |
-| DPP | 0.3 | 0.0615 | 0.0466 | 0.404 | 0.069 |
-| DPP | 0.4 | 0.0620 | 0.0471 | 0.418 | 0.072 |
-| DPP | 0.5 | 0.0627 | 0.0478 | 0.439 | 0.078 |
-| DPP | 0.6 | 0.0629 | 0.0477 | 0.473 | 0.088 |
-| DPP | 0.7 | 0.0648 | 0.0484 | 0.519 | 0.104 |
-| DPP | 0.8 | 0.0616 | 0.0462 | 0.582 | 0.140 |
-| DPP | 0.9 | 0.0521 | 0.0398 | 0.667 | 0.251 |
-| DPP | 1.0 | 0.0151 | 0.0167 | 0.734 | 0.406 |
-| MMR | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
-| MMR | 0.1 | 0.0551 | 0.0423 | 0.332 | 0.045 |
-| MMR | 0.2 | 0.0553 | 0.0427 | 0.333 | 0.046 |
-| MMR | 0.3 | 0.0560 | 0.0429 | 0.335 | 0.046 |
-| MMR | 0.4 | 0.0566 | 0.0435 | 0.338 | 0.047 |
-| MMR | 0.5 | 0.0569 | 0.0442 | 0.341 | 0.048 |
-| MMR | 0.6 | 0.0575 | 0.0445 | 0.348 | 0.051 |
-| MMR | 0.7 | 0.0599 | 0.0446 | 0.360 | 0.055 |
-| MMR | 0.8 | 0.0606 | 0.0445 | 0.389 | 0.063 |
-| MMR | 0.9 | 0.0567 | 0.0420 | 0.484 | 0.102 |
-| MMR | 1.0 | 0.0156 | 0.0162 | 0.721 | 0.467 |
-| MSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
-| MSD | 0.1 | 0.0555 | 0.0428 | 0.368 | 0.045 |
-| MSD | 0.2 | 0.0567 | 0.0434 | 0.416 | 0.045 |
-| MSD | 0.3 | 0.0581 | 0.0429 | 0.472 | 0.046 |
-| MSD | 0.4 | 0.0575 | 0.0426 | 0.536 | 0.047 |
-| MSD | 0.5 | 0.0553 | 0.0417 | 0.600 | 0.046 |
-| MSD | 0.6 | 0.0512 | 0.0392 | 0.660 | 0.046 |
-| MSD | 0.7 | 0.0483 | 0.0368 | 0.711 | 0.047 |
-| MSD | 0.8 | 0.0411 | 0.0324 | 0.752 | 0.047 |
-| MSD | 0.9 | 0.0258 | 0.0215 | 0.783 | 0.048 |
-| MSD | 1.0 | 0.0132 | 0.0132 | 0.801 | 0.050 |
-| SSD | 0.0 | 0.0547 | 0.0422 | 0.331 | 0.045 |
-| SSD | 0.1 | 0.0558 | 0.0428 | 0.333 | 0.045 |
-| SSD | 0.2 | 0.0565 | 0.0431 | 0.335 | 0.046 |
-| SSD | 0.3 | 0.0574 | 0.0438 | 0.337 | 0.046 |
-| SSD | 0.4 | 0.0579 | 0.0445 | 0.341 | 0.048 |
-| SSD | 0.5 | 0.0594 | 0.0449 | 0.346 | 0.049 |
-| SSD | 0.6 | 0.0612 | 0.0456 | 0.354 | 0.051 |
-| SSD | 0.7 | 0.0607 | 0.0462 | 0.371 | 0.055 |
-| SSD | 0.8 | 0.0634 | 0.0472 | 0.415 | 0.064 |
-| SSD | 0.9 | 0.0537 | 0.0419 | 0.596 | 0.112 |
-| SSD | 1.0 | 0.0149 | 0.0166 | 0.736 | 0.405 |
-
-</details>
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From d53c5dee7e1306241578ab974e9442fd6907cb70 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 13:08:32 +0100
Subject: [PATCH 09/69] Updated benchmarks

---
 benchmarks/README.md | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index d9c15bf..7744aff 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -47,14 +47,16 @@ The Pareto area measures the relevance-diversity tradeoff quality by computing t
 
 ### Recommendations
 
-| Goal | Strategy | λ | Why |
-|-----------|----------|---|-----|
+| Goal | Strategy | diversity | Why |
+|-----------|----------|-----------|-----|
 | Balanced tradeoff | **DPP** | 0.4-0.6 | Highest combined Pareto area |
-| Maximum relevance, light diversity | **SSD** | 0.7-0.9 | Best nDCG at low diversity |
-| High average diversity | **MSD** | 0.3-0.5 | Dominates ILAD metric |
-| No similar pairs (strict) | **DPP** | 0.3-0.5 | Best ILMD scores |
-| Extreme diversity | **MSD** | 0.1-0.3 | Only strategy reaching ILAD > 0.9 |
-| Simple baseline | **MMR** | 0.5-0.7 | Easy to implement and tune |
+| Maximum relevance, light diversity | **SSD** | 0.1-0.3 | Best nDCG at low diversity |
+| High average diversity | **MSD** | 0.5-0.7 | Dominates ILAD metric |
+| No similar pairs (strict) | **DPP** | 0.5-0.7 | Best ILMD scores |
+| Extreme diversity | **MSD** | 0.7-0.9 | Only strategy reaching ILAD > 0.9 |
+| Simple baseline | **MMR** | 0.3-0.5 | Easy to implement and tune |
+
+*`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
 ## Relevance-Diversity Tradeoff
 
@@ -152,7 +154,7 @@ python -m benchmarks report
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=20 items selected by each strategy
-- **λ sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
+- **diversity sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
 <details>
 <summary>Programmatic API</summary>

From a818c61192bbcf98ef313372d56f52c11b381ac7 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 13:10:37 +0100
Subject: [PATCH 10/69] Updated benchmarks

---
 benchmarks/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 7744aff..ecf4b6c 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -43,7 +43,7 @@ The Pareto area measures the relevance-diversity tradeoff quality by computing t
 **Key insight:** The "best" strategy depends on your diversity metric:
 - **DPP** wins on combined score—best overall tradeoff
 - **MSD** dominates ILAD—great for maximum variety, but poor ILMD (allows similar pairs)
-- **SSD/DPP** better ensure no very similar pairs slip through
+- **DPP** is best for ILMD—ensures no very similar pairs slip through
 
 ### Recommendations
 

From 8cb553aa08f9a5b6699d2c49a6b6ff615e56a4f1 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 13:14:25 +0100
Subject: [PATCH 11/69] Added note on SSD

---
 benchmarks/README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index ecf4b6c..4a0cc2c 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -45,6 +45,11 @@ The Pareto area measures the relevance-diversity tradeoff quality by computing t
 - **MSD** dominates ILAD—great for maximum variety, but poor ILMD (allows similar pairs)
 - **DPP** is best for ILMD—ensures no very similar pairs slip through
 
+> **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
+> **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
+> recently shown items. For content feeds, infinite scroll, or conversational RAG where you
+> maintain a sliding window of recent items, SSD may outperform these results.
+
 ### Recommendations
 
 | Goal | Strategy | diversity | Why |

From e9b145ea0e27648370e0ead6a575ea8bc3563bb9 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:28:25 +0100
Subject: [PATCH 12/69] Updated results

---
 benchmarks/README.md           |  57 +--
 benchmarks/core/report.py      | 635 ++++++---------------------------
 benchmarks/results/latency.png | Bin 76195 -> 77162 bytes
 3 files changed, 135 insertions(+), 557 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 4a0cc2c..97dbf9b 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -29,21 +29,33 @@ We evaluate using two diversity metrics that capture different aspects:
 - **ILAD** (Intra-List Average Distance): Measures overall diversity spread
 - **ILMD** (Intra-List Minimum Distance): Ensures no very similar item pairs (worst-case diversity)
 
-### Pareto Area Under Curve
+### Dataset Wins by Region
 
-The Pareto area measures the relevance-diversity tradeoff quality by computing the area under each strategy's curve in the **diversification region** (diversity ≥ 0.3). Higher = better.
+We count how many datasets each strategy achieves the highest nDCG in each diversity region:
 
-| Strategy | nDCG vs ILAD | nDCG vs ILMD | Combined | Notes |
-|----------|:------------:|:------------:|:--------:|-------|
-| **DPP**  | 0.042        | 0.036        | **0.039** | Best overall balance |
-| **MSD**  | **0.046**    | 0.010        | 0.028    | Best for ILAD (overall variety) |
-| **SSD**  | 0.041        | 0.019        | 0.030    | Good balance |
-| **MMR**  | 0.038        | 0.029        | 0.033    | Simple baseline |
+**ILAD (Average Diversity):**
 
-**Key insight:** The "best" strategy depends on your diversity metric:
-- **DPP** wins on combined score—best overall tradeoff
-- **MSD** dominates ILAD—great for maximum variety, but poor ILMD (allows similar pairs)
-- **DPP** is best for ILMD—ensures no very similar pairs slip through
+| Region   | Range     | MMR | MSD | DPP | SSD | Best   |
+|----------|-----------|:---:|:---:|:---:|:---:|--------|
+| Low      | 0.3-0.5   | 0   | 1   | 1   | 1   | Tied   |
+| Moderate | 0.5-0.7   | 0   | 2   | 1   | 0   | **MSD** |
+| High     | 0.7-0.9   | 1   | 3   | 0   | 0   | **MSD** |
+
+**ILMD (Minimum Diversity):**
+
+| Region   | Range       | MMR | MSD | DPP | SSD | Best   |
+|----------|-------------|:---:|:---:|:---:|:---:|--------|
+| Low      | 0.05-0.15   | 1   | 0   | 2   | 0   | **DPP** |
+| Moderate | 0.15-0.25   | 1   | 0   | 1   | 0   | Tied   |
+| High     | 0.25-0.35   | 1   | 0   | 3   | 0   | **DPP** |
+
+**Total Wins:** DPP (8) > MSD (6) > MMR (4) > SSD (1)
+
+**Key insight:**
+- **DPP** wins overall—best at balancing relevance with ILMD (no similar pairs)
+- **MSD** dominates ILAD—best when you want maximum variety
+- **MMR** is a solid baseline with wins across regions
+- **SSD** shines in sequence-aware settings (see note below)
 
 > **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
 > **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
@@ -54,11 +66,10 @@ The Pareto area measures the relevance-diversity tradeoff quality by computing t
 
 | Goal | Strategy | diversity | Why |
 |-----------|----------|-----------|-----|
-| Balanced tradeoff | **DPP** | 0.4-0.6 | Highest combined Pareto area |
-| Maximum relevance, light diversity | **SSD** | 0.1-0.3 | Best nDCG at low diversity |
-| High average diversity | **MSD** | 0.5-0.7 | Dominates ILAD metric |
-| No similar pairs (strict) | **DPP** | 0.5-0.7 | Best ILMD scores |
-| Extreme diversity | **MSD** | 0.7-0.9 | Only strategy reaching ILAD > 0.9 |
+| Maximum variety | **MSD** | 0.7-1.0 | Dominates ILAD across all regions |
+| Avoid similar pairs | **DPP** | 0.5-0.8 | Best ILMD wins, balances relevance |
+| Balanced tradeoff | **DPP** | 0.4-0.6 | Most total wins (8), good on both metrics |
+| Sequence-aware feeds | **SSD** | 0.3-0.5 | Use with `recent_embeddings` |
 | Simple baseline | **MMR** | 0.3-0.5 | Easy to implement and tune |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
@@ -133,12 +144,12 @@ python -m benchmarks report
 
 ### Per-Dataset Winners (by ILAD region)
 
-| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Max (0.9+) |
-|---------|:-------------:|:------------------:|:--------------:|:----------:|
-| MovieLens-32M | SSD | DPP | MSD | - |
-| Last.FM | SSD | MSD | MSD | MSD |
-| Amazon-VG | - | - | MMR | MSD |
-| Goodreads | SSD | MSD | MSD | MSD |
+| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) |
+|---------|:-------------:|:------------------:|:--------------:|
+| MovieLens-32M | DPP | DPP | MSD |
+| Last.FM | SSD | MSD | MSD |
+| Amazon-VG | - | - | MMR |
+| Goodreads | MSD | MSD | MSD |
 
 *Raw JSON results are in `results/*.json`*
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 4b5a552..0f0d64b 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -1,4 +1,4 @@
-"""Markdown report and plot generation from benchmark results."""
+"""Plot generation from benchmark results."""
 
 from __future__ import annotations
 
@@ -10,545 +10,66 @@
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
-
-def _compute_retention_table(results: list[dict], retention_pct: float = 0.95) -> dict:
-    """Compute diversity achieved at given relevance retention threshold."""
-    table: dict[str, dict[str, dict[str, float]]] = {}
-
-    for dataset_result in results:
-        dataset = dataset_result["dataset"]
-        table[dataset] = {}
-
-        for strategy in STRATEGIES:
-            runs = [run for run in dataset_result["results"] if run["strategy"] == strategy]
-            baseline = next((run for run in runs if run["diversity"] == 0.0), None)
-
-            if not baseline:
-                continue
-
-            threshold = baseline["ndcg@10"] * retention_pct
-            valid = [run for run in runs if run["ndcg@10"] >= threshold and run["diversity"] > 0]
-
-            if valid:
-                best = max(valid, key=lambda run: run["ilad"])
-                table[dataset][strategy] = {
-                    "ilad": best["ilad"],
-                    "lambda": best["diversity"],
-                    "ndcg": best["ndcg@10"],
-                    "retention": best["ndcg@10"] / baseline["ndcg@10"],
-                }
-
-    return table
-
-
-def _extract_all_points(results: list[dict]) -> list[dict]:
-    """Extract all data points from results into flat list."""
-    all_points = []
-    for dataset_result in results:
-        dataset = dataset_result["dataset"]
-        for run in dataset_result["results"]:
-            all_points.append(
-                {
-                    "dataset": dataset,
-                    "strategy": run["strategy"],
-                    "ndcg": run["ndcg@10"],
-                    "ilad": run["ilad"],
-                }
-            )
-    return all_points
-
-
-def _find_regional_winners(
-    datasets: list[str],
-    all_points: list[dict],
-    regions: dict[str, tuple[float, float]],
-) -> tuple[dict, dict]:
-    """Find winner per region per dataset and aggregate wins."""
-    regional_data: dict[str, dict[str, dict[str, str | float]]] = {}
-    region_wins: dict[str, dict[str, int]] = {region: {} for region in regions}
-
-    for dataset in datasets:
-        dataset_points = [p for p in all_points if p["dataset"] == dataset]
-        regional_data[dataset] = {}
-
-        for region_name, (lo, hi) in regions.items():
-            in_region = [p for p in dataset_points if lo <= p["ilad"] < hi]
-            if in_region:
-                best = max(in_region, key=lambda x: x["ndcg"])
-                regional_data[dataset][region_name] = {
-                    "winner": best["strategy"],
-                    "ndcg": best["ndcg"],
-                    "ilad": best["ilad"],
-                }
-                winner = str(best["strategy"])
-                region_wins[region_name][winner] = region_wins[region_name].get(winner, 0) + 1
-
-    return regional_data, region_wins
-
-
-def _compute_region_averages(
-    datasets: list[str],
-    all_points: list[dict],
-    regions: dict[str, tuple[float, float]],
-) -> dict[str, dict[str, float]]:
-    """Compute average best nDCG per strategy per region across datasets."""
-    all_strategies = {"ssd", "dpp", "msd", "mmr"}
-    region_best_per_dataset: dict[str, dict[str, dict[str, float]]] = {r: {} for r in regions}
-
-    for dataset in datasets:
-        dataset_points = [p for p in all_points if p["dataset"] == dataset]
-        for region_name, (lo, hi) in regions.items():
-            in_region = [p for p in dataset_points if lo <= p["ilad"] < hi]
-            strategy_best: dict[str, float] = {}
-            for point in in_region:
-                s = point["strategy"]
-                if s not in strategy_best or point["ndcg"] > strategy_best[s]:
-                    strategy_best[s] = point["ndcg"]
-            if strategy_best:
-                region_best_per_dataset[region_name][dataset] = strategy_best
-
-    region_avg: dict[str, dict[str, float]] = {}
-    for region, dataset_bests in region_best_per_dataset.items():
-        valid = [d for d in dataset_bests.values() if set(d.keys()) == all_strategies]
-        if not valid:
-            valid = list(dataset_bests.values())
-
-        strategy_scores: dict[str, list[float]] = {}
-        for ds_bests in valid:
-            for s, score in ds_bests.items():
-                strategy_scores.setdefault(s, []).append(score)
-        region_avg[region] = {s: sum(v) / len(v) for s, v in strategy_scores.items() if v}
-
-    return region_avg
-
-
-def _compute_regional_winners(results: list[dict]) -> dict:
-    """Compute which strategy achieves best nDCG in each diversity region."""
-    regions = {
-        "Low (0.3-0.5)": (0.3, 0.5),
-        "Moderate (0.5-0.7)": (0.5, 0.7),
-        "High (0.7-0.9)": (0.7, 0.9),
-        "Maximum (0.9+)": (0.9, 1.0),
-    }
-
-    all_points = _extract_all_points(results)
-    datasets = sorted(set(p["dataset"] for p in all_points))
-
-    regional_data, region_wins = _find_regional_winners(datasets, all_points, regions)
-    region_avg = _compute_region_averages(datasets, all_points, regions)
-
-    return {"per_dataset": regional_data, "aggregate": region_wins, "avg_ndcg": region_avg}
-
-
-def _format_retention_table(retention_table: dict) -> list[str]:
-    """Format the retention table as markdown lines."""
-    lines = [
-        "## Diversity at 95% Relevance Retention\n",
-        "How much diversity (ILAD) can each strategy achieve while maintaining ≥95% of baseline nDCG?\n",
-        "| Dataset | MMR | MSD | DPP | SSD | Winner |",
-        "|---------|:---:|:---:|:---:|:---:|:------:|",
-    ]
-
-    for dataset in sorted(retention_table.keys()):
-        row = [dataset]
-        best_ilad = 0
-        winner = ""
-
-        for strategy in STRATEGIES:
-            if strategy in retention_table[dataset]:
-                ilad = retention_table[dataset][strategy]["ilad"]
-                row.append(f"{ilad:.3f}")
-                if ilad > best_ilad:
-                    best_ilad = ilad
-                    winner = strategy.upper()
-            else:
-                row.append("-")
-
-        row.append(f"**{winner}**")
-        lines.append("| " + " | ".join(row) + " |")
-
-    lines.append("")
-    return lines
-
-
-def _format_regional_analysis(regional_data: dict) -> list[str]:
-    """Format the regional winner analysis as markdown."""
-    lines = [
-        "## Best Strategy by Diversity Level\n",
-        "Which strategy achieves highest relevance (nDCG) at each diversity (ILAD) level?\n",
-        "| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) | Maximum (0.9+) |",
-        "|---------|:-------------:|:------------------:|:--------------:|:--------------:|",
-    ]
-
-    region_order = ["Low (0.3-0.5)", "Moderate (0.5-0.7)", "High (0.7-0.9)", "Maximum (0.9+)"]
-
-    for dataset in sorted(regional_data["per_dataset"].keys()):
-        row = [dataset]
-        for region in region_order:
-            if region in regional_data["per_dataset"][dataset]:
-                winner = regional_data["per_dataset"][dataset][region]["winner"].upper()
-                row.append(f"**{winner}**")
-            else:
-                row.append("-")
-        lines.append("| " + " | ".join(row) + " |")
-
-    # Add summary using best nDCG per region (averaged across datasets)
-    lines.append("")
-    lines.append("### Summary: Best nDCG per Strategy by Region\n")
-    lines.append("For each region, what's the best nDCG each strategy can achieve (averaged across datasets)?\n")
-    lines.append("| Region | SSD | DPP | MSD | MMR | Best |")
-    lines.append("|--------|:---:|:---:|:---:|:---:|:----:|")
-
-    avg_ndcg = regional_data.get("avg_ndcg", {})
-
-    for region in region_order:
-        region_avgs = avg_ndcg.get(region, {})
-        row = [region]
-        best_strategy = ""
-        best_val = -1.0
-        for s in ["ssd", "dpp", "msd", "mmr"]:
-            val = region_avgs.get(s, 0)
-            if val > 0:
-                row.append(f"{val:.3f}")
-                if val > best_val:
-                    best_val = val
-                    best_strategy = s.upper()
-            else:
-                row.append("-")
-        row.append(f"**{best_strategy}**" if best_strategy else "-")
-        lines.append("| " + " | ".join(row) + " |")
-
-    lines.append("")
-    return lines
-
-
-def _trapezoidal_area(points: list[dict]) -> float:
-    """Compute area under curve using trapezoidal rule."""
-    if len(points) < 2:
-        return 0.0
-    sorted_pts = sorted(points, key=lambda p: p["ilad"])
-    area = 0.0
-    for i in range(len(sorted_pts) - 1):
-        dx = sorted_pts[i + 1]["ilad"] - sorted_pts[i]["ilad"]
-        avg_y = (sorted_pts[i]["ndcg"] + sorted_pts[i + 1]["ndcg"]) / 2
-        area += dx * avg_y
-    return area
-
-
-def _find_overlap_range(strategy_points: dict[str, list[dict]]) -> tuple[float, float]:
-    """Find ILAD range where all strategies have data points."""
-    max_mins = []
-    min_maxs = []
-    for points in strategy_points.values():
-        if points:
-            ilads = [p["ilad"] for p in points]
-            max_mins.append(min(ilads))
-            min_maxs.append(max(ilads))
-
-    if max_mins and min_maxs:
-        return max(max_mins), min(min_maxs)
-    return 0.0, 1.0
-
-
-def _compute_pareto_area(results: list[dict]) -> dict:
-    """
-    Compute area under each strategy's Pareto curve per dataset.
-
-    Uses trapezoidal integration, sorted by ILAD.
-    Higher area = better overall relevance-diversity tradeoff.
-    """
-    strategies = ["mmr", "msd", "dpp", "ssd"]
-
-    # Collect points per dataset per strategy
-    dataset_strategy_points: dict[str, dict[str, list[dict]]] = {}
-    for dataset_result in results:
-        dataset = dataset_result["dataset"]
-        dataset_strategy_points[dataset] = {s: [] for s in strategies}
-        for run in dataset_result["results"]:
-            dataset_strategy_points[dataset][run["strategy"]].append({"ndcg": run["ndcg@10"], "ilad": run["ilad"]})
-
-    full_areas: dict[str, dict[str, float]] = {s: {} for s in strategies}
-    overlap_areas: dict[str, dict[str, float]] = {s: {} for s in strategies}
-
-    for dataset, strategy_points in dataset_strategy_points.items():
-        overlap_lo, overlap_hi = _find_overlap_range(strategy_points)
-        for s, points in strategy_points.items():
-            full_areas[s][dataset] = _trapezoidal_area(points)
-            overlap_pts = [p for p in points if overlap_lo <= p["ilad"] <= overlap_hi]
-            overlap_areas[s][dataset] = _trapezoidal_area(overlap_pts)
-
-    # Average across datasets
-    avg_full = {s: sum(full_areas[s].values()) / len(full_areas[s]) if full_areas[s] else 0.0 for s in strategies}
-    avg_overlap = {
-        s: sum(overlap_areas[s].values()) / len(overlap_areas[s]) if overlap_areas[s] else 0.0 for s in strategies
-    }
-
-    return {
-        "full_per_dataset": full_areas,
-        "overlap_per_dataset": overlap_areas,
-        "avg_full": avg_full,
-        "avg_overlap": avg_overlap,
-    }
-
-
-def _compute_pareto_dominance(results: list[dict]) -> dict:
+# Diversity regions for analysis
+ILAD_REGIONS = {
+    "low": (0.3, 0.5),
+    "moderate": (0.5, 0.7),
+    "high": (0.7, 0.9),
+}
+ILMD_REGIONS = {
+    "low": (0.05, 0.15),
+    "moderate": (0.15, 0.25),
+    "high": (0.25, 0.35),
+}
+
+
+def _compute_regional_winners(
+    all_data: list[dict],
+) -> dict[str, dict[str, dict[str, int]]]:
     """
-    Compute Pareto frontier dominance for each strategy.
+    Count dataset wins for each strategy in each diversity region.
 
-    A point is on the Pareto frontier if no other point has both higher nDCG
-    and higher ILAD. Returns count of frontier points per strategy per dataset.
+    A "win" means having the highest average nDCG for that region on a specific dataset.
+    Returns dict mapping metric -> region -> strategy -> win_count.
     """
-    strategies = ["mmr", "msd", "dpp", "ssd"]
-
-    # Collect all points per dataset
-    dataset_points: dict[str, list[dict]] = {}
-    for dataset_result in results:
-        dataset = dataset_result["dataset"]
-        dataset_points[dataset] = []
-        for run in dataset_result["results"]:
-            dataset_points[dataset].append(
-                {
-                    "strategy": run["strategy"],
-                    "ndcg": run["ndcg@10"],
-                    "ilad": run["ilad"],
-                }
-            )
+    results: dict[str, dict[str, dict[str, int]]] = {"ilad": {}, "ilmd": {}}
+    datasets = sorted(set(p["dataset"] for p in all_data))
 
-    # Compute Pareto frontier for each dataset
-    frontier_counts: dict[str, dict[str, int]] = {s: {} for s in strategies}
-    total_frontier: dict[str, int] = {s: 0 for s in strategies}
+    for metric, regions in [("ilad", ILAD_REGIONS), ("ilmd", ILMD_REGIONS)]:
+        for region_name, (low, high) in regions.items():
+            win_counts: dict[str, int] = {s: 0 for s in STRATEGIES}
 
-    for dataset, points in dataset_points.items():
-        for point in points:
-            is_dominated = False
-            for other in points:
-                if other is point:
+            for dataset in datasets:
+                # Get points for this dataset in this region
+                region_points = [p for p in all_data if p["dataset"] == dataset and low <= p[metric] < high]
+                if not region_points:
                     continue
-                # Check if 'other' dominates 'point' (better or equal on both, strictly better on at least one)
-                if other["ndcg"] >= point["ndcg"] and other["ilad"] >= point["ilad"]:
-                    if other["ndcg"] > point["ndcg"] or other["ilad"] > point["ilad"]:
-                        is_dominated = True
-                        break
-
-            if not is_dominated:
-                strategy = point["strategy"]
-                frontier_counts[strategy][dataset] = frontier_counts[strategy].get(dataset, 0) + 1
-                total_frontier[strategy] += 1
-
-    # Compute percentage of all frontier points
-    total_points = sum(total_frontier.values())
-    frontier_pct: dict[str, float] = {
-        s: (count / total_points * 100) if total_points > 0 else 0.0 for s, count in total_frontier.items()
-    }
 
-    return {
-        "per_dataset": frontier_counts,
-        "total": total_frontier,
-        "percentage": frontier_pct,
-    }
+                # Compute avg nDCG per strategy for this dataset+region
+                strategy_ndcg: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+                for point in region_points:
+                    strategy_ndcg[point["strategy"]].append(point["ndcg"])
 
+                # Find winner for this dataset
+                best_strategy = None
+                best_ndcg = -1.0
+                for strategy, ndcgs in strategy_ndcg.items():
+                    if ndcgs:
+                        avg = sum(ndcgs) / len(ndcgs)
+                        if avg > best_ndcg:
+                            best_ndcg = avg
+                            best_strategy = strategy
 
-def _compute_multi_metric_summary(results: list[dict], min_diversity: float = 0.3) -> dict:
-    """
-    Compute Pareto area for nDCG vs ILAD and nDCG vs ILMD.
+                if best_strategy:
+                    win_counts[best_strategy] += 1
 
-    Only counts points in the diversification region (diversity >= min_diversity)
-    to avoid rewarding strategies that simply don't diversify.
-    """
-    strategies = ["mmr", "msd", "dpp", "ssd"]
-    metric_pairs = [
-        ("ndcg@10", "ilad", "nDCG vs ILAD"),
-        ("ndcg@10", "ilmd", "nDCG vs ILMD"),
-    ]
-
-    summary: dict[str, dict[str, float]] = {label: {} for _, _, label in metric_pairs}
-
-    for rel_key, div_key, label in metric_pairs:
-        strategy_areas: dict[str, list[float]] = {s: [] for s in strategies}
-
-        for dataset_result in results:
-            for s in strategies:
-                # Filter to only include points in the diversification region
-                points = [
-                    {rel_key: run[rel_key], div_key: run[div_key]}
-                    for run in dataset_result["results"]
-                    if run["strategy"] == s and run[div_key] >= min_diversity
-                ]
-                area = _trapezoidal_area_generic(points, div_key, rel_key)
-                strategy_areas[s].append(area)
-
-        # Average across datasets
-        for s in strategies:
-            summary[label][s] = sum(strategy_areas[s]) / len(strategy_areas[s]) if strategy_areas[s] else 0.0
-
-    return summary
-
-
-def _trapezoidal_area_generic(points: list[dict], x_key: str, y_key: str) -> float:
-    """Compute area under curve using trapezoidal rule with configurable keys."""
-    if len(points) < 2:
-        return 0.0
-    sorted_pts = sorted(points, key=lambda p: p[x_key])
-    area = 0.0
-    for i in range(len(sorted_pts) - 1):
-        dx = sorted_pts[i + 1][x_key] - sorted_pts[i][x_key]
-        avg_y = (sorted_pts[i][y_key] + sorted_pts[i + 1][y_key]) / 2
-        area += dx * avg_y
-    return area
-
-
-def _format_multi_metric_table(multi_metric: dict) -> list[str]:
-    """Format multi-metric summary as markdown table with combined score."""
-    lines = [
-        "### Multi-Metric Comparison\n",
-        "Area under Pareto curve (AUC) for different metric combinations:\n",
-        "| Metrics | SSD | DPP | MSD | MMR | Best |",
-        "|---------|:---:|:---:|:---:|:---:|:----:|",
-    ]
-
-    strategies = ["ssd", "dpp", "msd", "mmr"]
-    combined_scores: dict[str, float] = {s: 0.0 for s in strategies}
-
-    for label, areas in multi_metric.items():
-        row = [label]
-        best_s = ""
-        best_val = -1.0
-        for s in strategies:
-            val = areas.get(s, 0)
-            row.append(f"{val:.4f}")
-            combined_scores[s] += val
-            if val > best_val:
-                best_val = val
-                best_s = s.upper()
-        row.append(f"**{best_s}**")
-        lines.append("| " + " | ".join(row) + " |")
-
-    # Add combined row (average across all metric pairs)
-    num_metrics = len(multi_metric)
-    row = ["**Combined (avg)**"]
-    best_s = ""
-    best_val = -1.0
-    for s in strategies:
-        avg_val = combined_scores[s] / num_metrics if num_metrics > 0 else 0.0
-        row.append(f"**{avg_val:.4f}**")
-        if avg_val > best_val:
-            best_val = avg_val
-            best_s = s.upper()
-    row.append(f"**{best_s}**")
-    lines.append("| " + " | ".join(row) + " |")
-
-    lines.append("")
-    lines.append("*Higher AUC = better overall tradeoff. Combined averages across all metric pairs.*")
-    lines.append("")
-    return lines
-
-
-def _format_pareto_summary(pareto_data: dict, area_data: dict) -> list[str]:
-    """Format Pareto dominance and area summary as markdown."""
-    lines = [
-        "## Overall Performance Summary\n",
-        "### Pareto Frontier Analysis\n",
-        "Points on the Pareto frontier represent optimal relevance-diversity tradeoffs.\n",
-        "| Strategy | Frontier Points | Share | Avg Area (Full) | Avg Area (Overlap) |",
-        "|----------|----------------:|------:|----------------:|-------------------:|",
-    ]
-
-    total = pareto_data["total"]
-    pct = pareto_data["percentage"]
-    avg_full = area_data["avg_full"]
-    avg_overlap = area_data["avg_overlap"]
-
-    # Sort by overlap area descending (fairest comparison)
-    for strategy in sorted(total.keys(), key=lambda s: avg_overlap.get(s, 0), reverse=True):
-        lines.append(
-            f"| **{strategy.upper()}** | {total[strategy]} | {pct[strategy]:.1f}% | "
-            f"{avg_full.get(strategy, 0):.4f} | {avg_overlap.get(strategy, 0):.4f} |"
-        )
+            results[metric][region_name] = win_counts
 
-    lines.extend(
-        [
-            "",
-            "*Full Area*: Area under each strategy's curve across all ILAD values it can reach.",
-            "*Overlap Area*: Area only in the ILAD range where all strategies compete (fairest comparison).",
-            "",
-        ]
-    )
-    return lines
-
-
-def _format_full_results(results: list[dict]) -> list[str]:
-    """Format full results as collapsible markdown."""
-    lines = [
-        "<details>",
-        "<summary>Full Results by Dataset (click to expand)</summary>\n",
-    ]
-
-    for dataset_result in sorted(results, key=lambda x: x["dataset"]):
-        dataset = dataset_result["dataset"]
-        lines.append(f"### {dataset}\n")
-        lines.append("| Strategy | λ | nDCG@10 | MRR | ILAD | ILMD |")
-        lines.append("|----------|--:|--------:|----:|-----:|-----:|")
-
-        for run in sorted(dataset_result["results"], key=lambda x: (x["strategy"], x["diversity"])):
-            lines.append(
-                f"| {run['strategy'].upper()} | {run['diversity']:.1f} | "
-                f"{run['ndcg@10']:.4f} | {run['mrr']:.4f} | {run['ilad']:.3f} | {run['ilmd']:.3f} |"
-            )
-        lines.append("")
-
-    lines.append("</details>\n")
-    return lines
-
-
-def generate_markdown(results: list[dict]) -> str:
-    """Generate markdown report content from benchmark results."""
-    retention_table = _compute_retention_table(results, retention_pct=0.95)
-    regional_data = _compute_regional_winners(results)
-
-    md_lines = [
-        "# Pyversity Benchmark Results\n",
-        "Comparison of diversification strategies across recommendation datasets.\n",
-        "## Datasets\n",
-        "| Dataset | Source | Description |",
-        "|---------|--------|-------------|",
-        "| MovieLens-32M | GroupLens | 32M ratings from MovieLens |",
-        "| Last.FM | HetRec 2011 | Music listening data |",
-        "| Amazon Video Games | McAuley Lab | Product reviews |",
-        "| Goodreads | McAuley Lab | Book ratings |",
-        "",
-        "## Strategies\n",
-        "| Strategy | Description | Complexity |",
-        "|----------|-------------|------------|",
-        "| **MMR** | Maximal Marginal Relevance | O(k·n) |",
-        "| **MSD** | Max-Sum Diversification | O(k·n) |",
-        "| **DPP** | Determinantal Point Process | O(k²·n) |",
-        "| **SSD** | Sliding Spectrum Decomposition | O(k²·n) |",
-        "",
-    ]
-
-    pareto_data = _compute_pareto_dominance(results)
-    area_data = _compute_pareto_area(results)
-    multi_metric = _compute_multi_metric_summary(results)
-    md_lines.extend(_format_pareto_summary(pareto_data, area_data))
-    md_lines.extend(_format_multi_metric_table(multi_metric))
-    md_lines.extend(_format_regional_analysis(regional_data))
-    md_lines.extend(_format_retention_table(retention_table))
-    md_lines.extend(_format_full_results(results))
-
-    return "\n".join(md_lines)
+    return results
 
 
 def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
-    """
-    Generate Pareto frontier plot showing relevance vs diversity tradeoff.
-
-    Args:
-    ----
-        all_data: List of benchmark results.
-        output_path: Path to save the plot.
-        diversity_metric: 'ilad' for average diversity, 'ilmd' for minimum diversity.
-
-    """
+    """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
     import matplotlib.pyplot as plt
 
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
@@ -650,8 +171,54 @@ def generate_latency_plot(output_path: Path) -> None:
     plt.close()
 
 
+def _log_regional_analysis(regional: dict, num_datasets: int) -> None:
+    """Log regional analysis tables."""
+    logger.info(f"\nRegional Analysis (dataset wins out of {num_datasets} datasets):")
+    logger.info("\nILAD (Average Diversity):")
+    logger.info("| Region   | Range     | MMR | MSD | DPP | SSD | Best   |")
+    logger.info("|----------|-----------|-----|-----|-----|-----|--------|")
+    for region in ["low", "moderate", "high"]:
+        if region in regional["ilad"]:
+            wins = regional["ilad"][region]
+            low, high = ILAD_REGIONS[region]
+            best = max(wins.items(), key=lambda x: x[1])[0] if wins else "-"
+            logger.info(
+                f"| {region:8} | {low:.1f}-{high:.1f}   | "
+                f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
+                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best.upper():6} |"
+            )
+
+    logger.info("\nILMD (Minimum Diversity):")
+    logger.info("| Region   | Range       | MMR | MSD | DPP | SSD | Best   |")
+    logger.info("|----------|-------------|-----|-----|-----|-----|--------|")
+    for region in ["low", "moderate", "high"]:
+        if region in regional["ilmd"]:
+            wins = regional["ilmd"][region]
+            low, high = ILMD_REGIONS[region]
+            best = max(wins.items(), key=lambda x: x[1])[0] if wins else "-"
+            logger.info(
+                f"| {region:8} | {low:.2f}-{high:.2f} | "
+                f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
+                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best.upper():6} |"
+            )
+
+    # Summary: total wins across all regions
+    logger.info("\nTotal Wins Summary:")
+    total_wins: dict[str, int] = {s: 0 for s in STRATEGIES}
+    for metric in ["ilad", "ilmd"]:
+        for region_wins in regional[metric].values():
+            for strategy, count in region_wins.items():
+                total_wins[strategy] += count
+
+    sorted_strategies = sorted(total_wins.items(), key=lambda x: x[1], reverse=True)
+    logger.info("| Strategy | Total Wins |")
+    logger.info("|----------|------------|")
+    for strategy, count in sorted_strategies:
+        logger.info(f"| {strategy.upper():8} | {count:10d} |")
+
+
 def generate_report(results_dir: Path) -> None:
-    """Generate markdown report and plots from JSON results."""
+    """Generate plots from JSON results. Main findings are in benchmarks/README.md."""
     results = []
     for json_path in results_dir.glob("*.json"):
         with open(json_path) as f:
@@ -661,7 +228,7 @@ def generate_report(results_dir: Path) -> None:
         logger.warning("No results found.")
         return
 
-    all_data = []
+    all_data: list[dict] = []
     for dataset_result in results:
         dataset = dataset_result["dataset"]
         for run in dataset_result["results"]:
@@ -677,11 +244,6 @@ def generate_report(results_dir: Path) -> None:
                 }
             )
 
-    md_content = generate_markdown(results)
-    report_path = results_dir / "RESULTS.md"
-    report_path.write_text(md_content)
-    logger.debug(f"Saved: {report_path}")
-
     # Generate ILAD plot (average diversity)
     pareto_ilad_path = results_dir / "pareto_ilad.png"
     generate_pareto_plot(all_data, pareto_ilad_path, diversity_metric="ilad")
@@ -696,4 +258,9 @@ def generate_report(results_dir: Path) -> None:
     generate_latency_plot(latency_path)
     logger.debug(f"Saved: {latency_path}")
 
-    logger.info(f"Report generated: {results_dir}")
+    # Regional analysis - count wins per dataset
+    regional = _compute_regional_winners(all_data)
+    num_datasets = len(set(p["dataset"] for p in all_data))
+    _log_regional_analysis(regional, num_datasets)
+
+    logger.info(f"\nReport generated: {results_dir}")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From 58c5c818a6bd693a3481c22cf8ad503f6f86afc2 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:31:44 +0100
Subject: [PATCH 13/69] Updated results

---
 benchmarks/README.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 97dbf9b..71e7cbe 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -166,12 +166,32 @@ python -m benchmarks report
 
 ## Methodology
 
+### Experimental Setup
+
 - **Evaluation**: Leave-one-out protocol with 2,000 sampled users per dataset
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=20 items selected by each strategy
 - **diversity sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
+### How We Compute "Wins"
+
+For each diversity region (e.g., ILAD 0.5-0.7), we:
+
+1. **Filter** each strategy's results to points within that region
+2. **Find best nDCG** achieved by each strategy in that region
+3. **Compare across strategies** per dataset—the strategy with highest nDCG "wins" that dataset
+4. **Count wins** across all 4 datasets to determine the overall leader
+
+This approach measures: *"At a given diversity level, which strategy maintains the best relevance?"*
+
+We use three regions per metric:
+- **Low**: Where diversification begins (ILAD 0.3-0.5, ILMD 0.05-0.15)
+- **Moderate**: Balanced tradeoff zone (ILAD 0.5-0.7, ILMD 0.15-0.25)
+- **High**: Strong diversity (ILAD 0.7-0.9, ILMD 0.25-0.35)
+
+*Note: Not all strategies reach all regions on all datasets. A "-" in the per-dataset table means no data points in that region.*
+
 <details>
 <summary>Programmatic API</summary>
 

From 8fce587335d8a265cd98a5e5dd3ffa8878808cf8 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:33:48 +0100
Subject: [PATCH 14/69] Updated results

---
 benchmarks/README.md | 26 +++++++++-----------------
 1 file changed, 9 insertions(+), 17 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 71e7cbe..659df4a 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -33,23 +33,15 @@ We evaluate using two diversity metrics that capture different aspects:
 
 We count how many datasets each strategy achieves the highest nDCG in each diversity region:
 
-**ILAD (Average Diversity):**
-
-| Region   | Range     | MMR | MSD | DPP | SSD | Best   |
-|----------|-----------|:---:|:---:|:---:|:---:|--------|
-| Low      | 0.3-0.5   | 0   | 1   | 1   | 1   | Tied   |
-| Moderate | 0.5-0.7   | 0   | 2   | 1   | 0   | **MSD** |
-| High     | 0.7-0.9   | 1   | 3   | 0   | 0   | **MSD** |
-
-**ILMD (Minimum Diversity):**
-
-| Region   | Range       | MMR | MSD | DPP | SSD | Best   |
-|----------|-------------|:---:|:---:|:---:|:---:|--------|
-| Low      | 0.05-0.15   | 1   | 0   | 2   | 0   | **DPP** |
-| Moderate | 0.15-0.25   | 1   | 0   | 1   | 0   | Tied   |
-| High     | 0.25-0.35   | 1   | 0   | 3   | 0   | **DPP** |
-
-**Total Wins:** DPP (8) > MSD (6) > MMR (4) > SSD (1)
+| Metric | Region   | Range       | MMR | MSD | DPP | SSD | Best    |
+|--------|----------|-------------|:---:|:---:|:---:|:---:|---------|
+| ILAD   | Low      | 0.3-0.5     | 0   | 1   | 1   | 1   | Tied    |
+| ILAD   | Moderate | 0.5-0.7     | 0   | 2   | 1   | 0   | **MSD** |
+| ILAD   | High     | 0.7-0.9     | 1   | 3   | 0   | 0   | **MSD** |
+| ILMD   | Low      | 0.05-0.15   | 1   | 0   | 2   | 0   | **DPP** |
+| ILMD   | Moderate | 0.15-0.25   | 1   | 0   | 1   | 0   | Tied    |
+| ILMD   | High     | 0.25-0.35   | 1   | 0   | 3   | 0   | **DPP** |
+| | | **Total** | **4** | **6** | **8** | **1** | **DPP** |
 
 **Key insight:**
 - **DPP** wins overall—best at balancing relevance with ILMD (no similar pairs)

From 95f84d377f9a90b7fe380a1a6bd2a43b1ea9d39e Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:40:47 +0100
Subject: [PATCH 15/69] Updated results

---
 benchmarks/README.md | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 659df4a..f51a7ff 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -43,6 +43,8 @@ We count how many datasets each strategy achieves the highest nDCG in each diver
 | ILMD   | High     | 0.25-0.35   | 1   | 0   | 3   | 0   | **DPP** |
 | | | **Total** | **4** | **6** | **8** | **1** | **DPP** |
 
+*Wins per region: dataset has a "win" if the strategy achieves highest avg nDCG in that diversity range. Some rows sum to <4 when a dataset has no data points in that region.*
+
 **Key insight:**
 - **DPP** wins overall—best at balancing relevance with ILMD (no similar pairs)
 - **MSD** dominates ILAD—best when you want maximum variety
@@ -177,10 +179,14 @@ For each diversity region (e.g., ILAD 0.5-0.7), we:
 
 This approach measures: *"At a given diversity level, which strategy maintains the best relevance?"*
 
-We use three regions per metric:
-- **Low**: Where diversification begins (ILAD 0.3-0.5, ILMD 0.05-0.15)
-- **Moderate**: Balanced tradeoff zone (ILAD 0.5-0.7, ILMD 0.15-0.25)
-- **High**: Strong diversity (ILAD 0.7-0.9, ILMD 0.25-0.35)
+We use three regions per metric based on observed ranges across all datasets:
+- **ILAD range**: 0.46–0.99 across all strategies/datasets → regions at 0.3-0.5, 0.5-0.7, 0.7-0.9
+- **ILMD range**: 0.00–0.73 across all strategies/datasets → regions at 0.05-0.15, 0.15-0.25, 0.25-0.35
+
+Regions:
+- **Low**: Where diversification begins
+- **Moderate**: Balanced tradeoff zone
+- **High**: Strong diversity
 
 *Note: Not all strategies reach all regions on all datasets. A "-" in the per-dataset table means no data points in that region.*
 

From 9a493b46cdc8535977eeb438bf523e496ae1eb21 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:42:59 +0100
Subject: [PATCH 16/69] Updated results

---
 benchmarks/README.md           |  14 +++++++-------
 benchmarks/core/report.py      |  15 ++++++++-------
 benchmarks/results/latency.png | Bin 77162 -> 77092 bytes
 3 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index f51a7ff..68f0915 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -35,13 +35,13 @@ We count how many datasets each strategy achieves the highest nDCG in each diver
 
 | Metric | Region   | Range       | MMR | MSD | DPP | SSD | Best    |
 |--------|----------|-------------|:---:|:---:|:---:|:---:|---------|
-| ILAD   | Low      | 0.3-0.5     | 0   | 1   | 1   | 1   | Tied    |
-| ILAD   | Moderate | 0.5-0.7     | 0   | 2   | 1   | 0   | **MSD** |
-| ILAD   | High     | 0.7-0.9     | 1   | 3   | 0   | 0   | **MSD** |
-| ILMD   | Low      | 0.05-0.15   | 1   | 0   | 2   | 0   | **DPP** |
-| ILMD   | Moderate | 0.15-0.25   | 1   | 0   | 1   | 0   | Tied    |
-| ILMD   | High     | 0.25-0.35   | 1   | 0   | 3   | 0   | **DPP** |
-| | | **Total** | **4** | **6** | **8** | **1** | **DPP** |
+| ILAD   | Low      | 0.45-0.60   | 0   | 2   | 1   | 0   | **MSD** |
+| ILAD   | Moderate | 0.60-0.75   | 0   | 3   | 0   | 0   | **MSD** |
+| ILAD   | High     | 0.75-1.00   | 0   | 3   | 1   | 0   | **MSD** |
+| ILMD   | Low      | 0.00-0.20   | 2   | 0   | 2   | 0   | Tied    |
+| ILMD   | Moderate | 0.20-0.45   | 1   | 0   | 3   | 0   | **DPP** |
+| ILMD   | High     | 0.45-0.75   | 2   | 0   | 2   | 0   | Tied    |
+| | | **Total** | **5** | **8** | **9** | **0** | **DPP** |
 
 *Wins per region: dataset has a "win" if the strategy achieves highest avg nDCG in that diversity range. Some rows sum to <4 when a dataset has no data points in that region.*
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 0f0d64b..76172f9 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -10,16 +10,17 @@
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
-# Diversity regions for analysis
+# Diversity regions for analysis (based on observed ranges across datasets)
+# ILAD observed: 0.46-0.99, ILMD observed: 0.00-0.73
 ILAD_REGIONS = {
-    "low": (0.3, 0.5),
-    "moderate": (0.5, 0.7),
-    "high": (0.7, 0.9),
+    "low": (0.45, 0.60),
+    "moderate": (0.60, 0.75),
+    "high": (0.75, 1.0),
 }
 ILMD_REGIONS = {
-    "low": (0.05, 0.15),
-    "moderate": (0.15, 0.25),
-    "high": (0.25, 0.35),
+    "low": (0.0, 0.20),
+    "moderate": (0.20, 0.45),
+    "high": (0.45, 0.75),
 }
 
 
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index ff6690401b91b804dea5579d3bfece0d9be727bf..3bc05b94f5ae8ce7390de48d2db8c30e5189eb18 100644
GIT binary patch
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From ad0bff234dd96f7368f9561ee3f2376599f5a090 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 14:44:57 +0100
Subject: [PATCH 17/69] Updated results

---
 benchmarks/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 68f0915..1967e0a 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -38,9 +38,9 @@ We count how many datasets each strategy achieves the highest nDCG in each diver
 | ILAD   | Low      | 0.45-0.60   | 0   | 2   | 1   | 0   | **MSD** |
 | ILAD   | Moderate | 0.60-0.75   | 0   | 3   | 0   | 0   | **MSD** |
 | ILAD   | High     | 0.75-1.00   | 0   | 3   | 1   | 0   | **MSD** |
-| ILMD   | Low      | 0.00-0.20   | 2   | 0   | 2   | 0   | Tied    |
+| ILMD   | Low      | 0.00-0.20   | 2   | 0   | 2   | 0   | MMR/DPP |
 | ILMD   | Moderate | 0.20-0.45   | 1   | 0   | 3   | 0   | **DPP** |
-| ILMD   | High     | 0.45-0.75   | 2   | 0   | 2   | 0   | Tied    |
+| ILMD   | High     | 0.45-0.75   | 2   | 0   | 2   | 0   | MMR/DPP |
 | | | **Total** | **5** | **8** | **9** | **0** | **DPP** |
 
 *Wins per region: dataset has a "win" if the strategy achieves highest avg nDCG in that diversity range. Some rows sum to <4 when a dataset has no data points in that region.*
@@ -180,8 +180,8 @@ For each diversity region (e.g., ILAD 0.5-0.7), we:
 This approach measures: *"At a given diversity level, which strategy maintains the best relevance?"*
 
 We use three regions per metric based on observed ranges across all datasets:
-- **ILAD range**: 0.46–0.99 across all strategies/datasets → regions at 0.3-0.5, 0.5-0.7, 0.7-0.9
-- **ILMD range**: 0.00–0.73 across all strategies/datasets → regions at 0.05-0.15, 0.15-0.25, 0.25-0.35
+- **ILAD range**: 0.46–0.99 across all strategies/datasets → regions at 0.45-0.60, 0.60-0.75, 0.75-1.00
+- **ILMD range**: 0.00–0.73 across all strategies/datasets → regions at 0.00-0.20, 0.20-0.45, 0.45-0.75
 
 Regions:
 - **Low**: Where diversification begins

From 0ac12885f5f7eba7c9a0636d25c2b80ac066d21b Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 15:04:14 +0100
Subject: [PATCH 18/69] Updated results

---
 benchmarks/README.md           |  52 +++++++++---------
 benchmarks/core/report.py      |  95 ++++++++++++++++++++++-----------
 benchmarks/results/latency.png | Bin 77092 -> 78661 bytes
 3 files changed, 90 insertions(+), 57 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1967e0a..cc43f60 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -31,24 +31,26 @@ We evaluate using two diversity metrics that capture different aspects:
 
 ### Dataset Wins by Region
 
-We count how many datasets each strategy achieves the highest nDCG in each diversity region:
+We count how many datasets each strategy achieves the **highest max nDCG** in each diversity region.
+Regions are computed dynamically by dividing the observed range into thirds:
 
 | Metric | Region   | Range       | MMR | MSD | DPP | SSD | Best    |
 |--------|----------|-------------|:---:|:---:|:---:|:---:|---------|
-| ILAD   | Low      | 0.45-0.60   | 0   | 2   | 1   | 0   | **MSD** |
-| ILAD   | Moderate | 0.60-0.75   | 0   | 3   | 0   | 0   | **MSD** |
-| ILAD   | High     | 0.75-1.00   | 0   | 3   | 1   | 0   | **MSD** |
-| ILMD   | Low      | 0.00-0.20   | 2   | 0   | 2   | 0   | MMR/DPP |
-| ILMD   | Moderate | 0.20-0.45   | 1   | 0   | 3   | 0   | **DPP** |
-| ILMD   | High     | 0.45-0.75   | 2   | 0   | 2   | 0   | MMR/DPP |
-| | | **Total** | **5** | **8** | **9** | **0** | **DPP** |
+| ILAD   | Low      | 0.33-0.55   | 0   | 1   | 1   | 1   | Tied    |
+| ILAD   | Moderate | 0.55-0.77   | 0   | 2   | 1   | 0   | **MSD** |
+| ILAD   | High     | 0.77-1.00   | 0   | 3   | 1   | 0   | **MSD** |
+| ILMD   | Low      | 0.04-0.29   | 1   | 1   | 1   | 1   | Tied    |
+| ILMD   | Moderate | 0.29-0.54   | 1   | 0   | 2   | 1   | **DPP** |
+| ILMD   | High     | 0.54-0.79   | 2   | 0   | 1   | 0   | **MMR** |
+| | | **Total** | **4** | **7** | **7** | **3** | **MSD/DPP** |
 
-*Wins per region: dataset has a "win" if the strategy achieves highest avg nDCG in that diversity range. Some rows sum to <4 when a dataset has no data points in that region.*
+*Wins per region: strategy with highest max nDCG in that diversity range wins. Some rows sum to <4 when a dataset has no data points in that region.*
 
 **Key insight:**
-- **DPP** wins overall—best at balancing relevance with ILMD (no similar pairs)
-- **MSD** dominates ILAD—best when you want maximum variety
-- **MMR** is a solid baseline with wins across regions
+- **MSD** and **DPP** tied at 7 wins each—but excel at different diversity metrics
+- **MSD** dominates ILAD—best for maximum overall variety
+- **DPP** excels at ILMD—best at ensuring no similar pairs slip through
+- **MMR** wins high ILMD regions—competitive simple baseline
 - **SSD** shines in sequence-aware settings (see note below)
 
 > **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
@@ -60,11 +62,11 @@ We count how many datasets each strategy achieves the highest nDCG in each diver
 
 | Goal | Strategy | diversity | Why |
 |-----------|----------|-----------|-----|
-| Maximum variety | **MSD** | 0.7-1.0 | Dominates ILAD across all regions |
-| Avoid similar pairs | **DPP** | 0.5-0.8 | Best ILMD wins, balances relevance |
-| Balanced tradeoff | **DPP** | 0.4-0.6 | Most total wins (8), good on both metrics |
+| Maximum variety | **MSD** | 0.7-1.0 | Dominates ILAD (best overall spread) |
+| Avoid similar pairs | **DPP** | 0.5-0.8 | Best ILMD in moderate region |
+| Balanced tradeoff | **DPP** | 0.4-0.6 | Strong on both ILAD and ILMD |
 | Sequence-aware feeds | **SSD** | 0.3-0.5 | Use with `recent_embeddings` |
-| Simple baseline | **MMR** | 0.3-0.5 | Easy to implement and tune |
+| Simple baseline | **MMR** | 0.3-0.5 | Easy to implement, competitive |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
@@ -173,20 +175,16 @@ python -m benchmarks report
 For each diversity region (e.g., ILAD 0.5-0.7), we:
 
 1. **Filter** each strategy's results to points within that region
-2. **Find best nDCG** achieved by each strategy in that region
-3. **Compare across strategies** per dataset—the strategy with highest nDCG "wins" that dataset
+2. **Find max nDCG** achieved by each strategy in that region
+3. **Compare across strategies** per dataset—the strategy with highest max nDCG "wins" that dataset
 4. **Count wins** across all 4 datasets to determine the overall leader
 
-This approach measures: *"At a given diversity level, which strategy maintains the best relevance?"*
+This approach measures: *"At a given diversity level, which strategy can achieve the best relevance?"*
 
-We use three regions per metric based on observed ranges across all datasets:
-- **ILAD range**: 0.46–0.99 across all strategies/datasets → regions at 0.45-0.60, 0.60-0.75, 0.75-1.00
-- **ILMD range**: 0.00–0.73 across all strategies/datasets → regions at 0.00-0.20, 0.20-0.45, 0.45-0.75
-
-Regions:
-- **Low**: Where diversification begins
-- **Moderate**: Balanced tradeoff zone
-- **High**: Strong diversity
+Regions are computed dynamically by dividing the observed metric range (across all strategies and datasets) into thirds:
+- **Low**: First third of observed range
+- **Moderate**: Middle third
+- **High**: Top third
 
 *Note: Not all strategies reach all regions on all datasets. A "-" in the per-dataset table means no data points in that region.*
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 76172f9..7dc9b51 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -10,33 +10,50 @@
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
-# Diversity regions for analysis (based on observed ranges across datasets)
-# ILAD observed: 0.46-0.99, ILMD observed: 0.00-0.73
-ILAD_REGIONS = {
-    "low": (0.45, 0.60),
-    "moderate": (0.60, 0.75),
-    "high": (0.75, 1.0),
-}
-ILMD_REGIONS = {
-    "low": (0.0, 0.20),
-    "moderate": (0.20, 0.45),
-    "high": (0.45, 0.75),
-}
+
+def _compute_dynamic_regions(all_data: list[dict], metric: str) -> dict[str, tuple[float, float]]:
+    """Compute diversity regions dynamically by dividing observed range into thirds."""
+    values = [p[metric] for p in all_data if metric in p]
+    if not values:
+        # Fallback defaults
+        if metric == "ilad":
+            return {"low": (0.45, 0.60), "moderate": (0.60, 0.75), "high": (0.75, 1.0)}
+        return {"low": (0.0, 0.20), "moderate": (0.20, 0.45), "high": (0.45, 0.75)}
+
+    min_val = min(values)
+    max_val = max(values)
+    step = (max_val - min_val) / 3
+
+    return {
+        "low": (min_val, min_val + step),
+        "moderate": (min_val + step, min_val + 2 * step),
+        "high": (min_val + 2 * step, max_val + 0.01),  # +0.01 to include max
+    }
 
 
 def _compute_regional_winners(
     all_data: list[dict],
-) -> dict[str, dict[str, dict[str, int]]]:
+) -> tuple[dict[str, dict[str, dict[str, int]]], dict[str, dict[str, tuple[float, float]]]]:
     """
     Count dataset wins for each strategy in each diversity region.
 
     A "win" means having the highest average nDCG for that region on a specific dataset.
-    Returns dict mapping metric -> region -> strategy -> win_count.
+
+    Returns
+    -------
+        - results: dict mapping metric -> region -> strategy -> win_count
+        - ranges: dict mapping metric -> region -> (low, high) bounds
+
     """
     results: dict[str, dict[str, dict[str, int]]] = {"ilad": {}, "ilmd": {}}
     datasets = sorted(set(p["dataset"] for p in all_data))
 
-    for metric, regions in [("ilad", ILAD_REGIONS), ("ilmd", ILMD_REGIONS)]:
+    # Compute dynamic regions based on actual data
+    ilad_regions = _compute_dynamic_regions(all_data, "ilad")
+    ilmd_regions = _compute_dynamic_regions(all_data, "ilmd")
+    all_regions = {"ilad": ilad_regions, "ilmd": ilmd_regions}
+
+    for metric, regions in [("ilad", ilad_regions), ("ilmd", ilmd_regions)]:
         for region_name, (low, high) in regions.items():
             win_counts: dict[str, int] = {s: 0 for s in STRATEGIES}
 
@@ -51,14 +68,14 @@ def _compute_regional_winners(
                 for point in region_points:
                     strategy_ndcg[point["strategy"]].append(point["ndcg"])
 
-                # Find winner for this dataset
+                # Find winner for this dataset (using max nDCG - best achievable)
                 best_strategy = None
                 best_ndcg = -1.0
                 for strategy, ndcgs in strategy_ndcg.items():
                     if ndcgs:
-                        avg = sum(ndcgs) / len(ndcgs)
-                        if avg > best_ndcg:
-                            best_ndcg = avg
+                        max_ndcg = max(ndcgs)
+                        if max_ndcg > best_ndcg:
+                            best_ndcg = max_ndcg
                             best_strategy = strategy
 
                 if best_strategy:
@@ -66,7 +83,18 @@ def _compute_regional_winners(
 
             results[metric][region_name] = win_counts
 
-    return results
+    return results, all_regions
+
+
+def _find_best_or_tied(wins: dict[str, int]) -> str:
+    """Find best strategy or list ties (e.g., 'DPP/MSD')."""
+    if not wins:
+        return "-"
+    max_wins = max(wins.values())
+    if max_wins == 0:
+        return "-"
+    winners = [s.upper() for s, count in wins.items() if count == max_wins]
+    return "/".join(sorted(winners))
 
 
 def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
@@ -172,7 +200,9 @@ def generate_latency_plot(output_path: Path) -> None:
     plt.close()
 
 
-def _log_regional_analysis(regional: dict, num_datasets: int) -> None:
+def _log_regional_analysis(
+    regional: dict, ranges: dict[str, dict[str, tuple[float, float]]], num_datasets: int
+) -> None:
     """Log regional analysis tables."""
     logger.info(f"\nRegional Analysis (dataset wins out of {num_datasets} datasets):")
     logger.info("\nILAD (Average Diversity):")
@@ -181,12 +211,12 @@ def _log_regional_analysis(regional: dict, num_datasets: int) -> None:
     for region in ["low", "moderate", "high"]:
         if region in regional["ilad"]:
             wins = regional["ilad"][region]
-            low, high = ILAD_REGIONS[region]
-            best = max(wins.items(), key=lambda x: x[1])[0] if wins else "-"
+            low, high = ranges["ilad"][region]
+            best = _find_best_or_tied(wins)
             logger.info(
-                f"| {region:8} | {low:.1f}-{high:.1f}   | "
+                f"| {region:8} | {low:.2f}-{high:.2f} | "
                 f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
-                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best.upper():6} |"
+                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best:6} |"
             )
 
     logger.info("\nILMD (Minimum Diversity):")
@@ -195,14 +225,19 @@ def _log_regional_analysis(regional: dict, num_datasets: int) -> None:
     for region in ["low", "moderate", "high"]:
         if region in regional["ilmd"]:
             wins = regional["ilmd"][region]
-            low, high = ILMD_REGIONS[region]
-            best = max(wins.items(), key=lambda x: x[1])[0] if wins else "-"
+            low, high = ranges["ilmd"][region]
+            best = _find_best_or_tied(wins)
             logger.info(
                 f"| {region:8} | {low:.2f}-{high:.2f} | "
                 f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
-                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best.upper():6} |"
+                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best:6} |"
             )
 
+    # Log the computed ranges
+    logger.info("\nDynamic ranges computed from data:")
+    logger.info(f"  ILAD: {ranges['ilad']}")
+    logger.info(f"  ILMD: {ranges['ilmd']}")
+
     # Summary: total wins across all regions
     logger.info("\nTotal Wins Summary:")
     total_wins: dict[str, int] = {s: 0 for s in STRATEGIES}
@@ -260,8 +295,8 @@ def generate_report(results_dir: Path) -> None:
     logger.debug(f"Saved: {latency_path}")
 
     # Regional analysis - count wins per dataset
-    regional = _compute_regional_winners(all_data)
+    regional, ranges = _compute_regional_winners(all_data)
     num_datasets = len(set(p["dataset"] for p in all_data))
-    _log_regional_analysis(regional, num_datasets)
+    _log_regional_analysis(regional, ranges, num_datasets)
 
     logger.info(f"\nReport generated: {results_dir}")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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DtG?YY


From a52de26ae3dc1f84958040eab2e26f6a1408ae30 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 15:12:15 +0100
Subject: [PATCH 19/69] Switched to budget relevance approach

---
 benchmarks/README.md           | 124 ++++++------
 benchmarks/core/report.py      | 337 +++++++++++++++++++++------------
 benchmarks/results/latency.png | Bin 78661 -> 77568 bytes
 3 files changed, 272 insertions(+), 189 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index cc43f60..1142319 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -25,51 +25,44 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We evaluate using two diversity metrics that capture different aspects:
-- **ILAD** (Intra-List Average Distance): Measures overall diversity spread
-- **ILMD** (Intra-List Minimum Distance): Ensures no very similar item pairs (worst-case diversity)
-
-### Dataset Wins by Region
-
-We count how many datasets each strategy achieves the **highest max nDCG** in each diversity region.
-Regions are computed dynamically by dividing the observed range into thirds:
-
-| Metric | Region   | Range       | MMR | MSD | DPP | SSD | Best    |
-|--------|----------|-------------|:---:|:---:|:---:|:---:|---------|
-| ILAD   | Low      | 0.33-0.55   | 0   | 1   | 1   | 1   | Tied    |
-| ILAD   | Moderate | 0.55-0.77   | 0   | 2   | 1   | 0   | **MSD** |
-| ILAD   | High     | 0.77-1.00   | 0   | 3   | 1   | 0   | **MSD** |
-| ILMD   | Low      | 0.04-0.29   | 1   | 1   | 1   | 1   | Tied    |
-| ILMD   | Moderate | 0.29-0.54   | 1   | 0   | 2   | 1   | **DPP** |
-| ILMD   | High     | 0.54-0.79   | 2   | 0   | 1   | 0   | **MMR** |
-| | | **Total** | **4** | **7** | **7** | **3** | **MSD/DPP** |
-
-*Wins per region: strategy with highest max nDCG in that diversity range wins. Some rows sum to <4 when a dataset has no data points in that region.*
-
-**Key insight:**
-- **MSD** and **DPP** tied at 7 wins each—but excel at different diversity metrics
-- **MSD** dominates ILAD—best for maximum overall variety
-- **DPP** excels at ILMD—best at ensuring no similar pairs slip through
-- **MMR** wins high ILMD regions—competitive simple baseline
-- **SSD** shines in sequence-aware settings (see note below)
+We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance**. This ensures we only compare "deployable" configurations.
 
-> **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
-> **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
-> recently shown items. For content feeds, infinite scroll, or conversational RAG where you
-> maintain a sliding window of recent items, SSD may outperform these results.
+### Strategy Scorecard
+
+| Strategy | Combined Score | Typical λ | Avg nDCG vs Baseline |
+|----------|:--------------:|:---------:|:--------------------:|
+| **DPP**  | **0.439**      | 0.85      | 99.3%                |
+| SSD      | 0.236          | 0.85      | 101.9%               |
+| MSD      | 0.226          | 0.50      | 98.7%                |
+| MMR      | 0.213          | 0.78      | 101.4%               |
+
+*Combined Score = geometric mean of normalized ILAD and ILMD gains. Higher = better balance of both diversity metrics.*
+
+### Best Configs Per Goal (≥95% baseline nDCG)
+
+| Goal | Winner | Wins | Why |
+|------|--------|:----:|-----|
+| **Best Overall** | **DPP** | 4/4 | Highest combined diversity (both ILAD and ILMD) |
+| **Max ILAD** (overall variety) | **MSD** | 3/4 | Optimizes sum of pairwise distances |
+| **Max ILMD** (no similar pairs) | **DPP** | 4/4 | Best worst-case diversity guarantee |
 
 ### Recommendations
 
-| Goal | Strategy | diversity | Why |
-|-----------|----------|-----------|-----|
-| Maximum variety | **MSD** | 0.7-1.0 | Dominates ILAD (best overall spread) |
-| Avoid similar pairs | **DPP** | 0.5-0.8 | Best ILMD in moderate region |
-| Balanced tradeoff | **DPP** | 0.4-0.6 | Strong on both ILAD and ILMD |
-| Sequence-aware feeds | **SSD** | 0.3-0.5 | Use with `recent_embeddings` |
-| Simple baseline | **MMR** | 0.3-0.5 | Easy to implement, competitive |
+| Goal | Strategy | `diversity` | Notes |
+|------|----------|:-----------:|-------|
+| **Best overall balance** | **DPP** | 0.8-0.9 | Wins both overall and ILMD |
+| **Maximum variety** | **MSD** | 0.5-0.6 | Best ILAD while keeping relevance |
+| **Avoid similar pairs** | **DPP** | 0.8-0.9 | Strictest duplicate avoidance |
+| **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
+| **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
+> **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
+> **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
+> recently shown items. For content feeds, infinite scroll, or conversational RAG where you
+> maintain a sliding window of recent items, SSD may outperform these results.
+
 ## Relevance-Diversity Tradeoff
 
 ### ILAD (Average Diversity)
@@ -130,22 +123,14 @@ python -m benchmarks report
 | **DPP** | Determinantal Point Process | O(k²·n) |
 | **SSD** | Sliding Spectrum Decomposition | O(k²·n·d) |
 
-### Metrics
-
-| Metric | Type | Description |
-|--------|------|-------------|
-| nDCG@k | Relevance | Normalized Discounted Cumulative Gain |
-| ILAD | Diversity | Intra-List Average Distance (mean pairwise) |
-| ILMD | Diversity | Intra-List Minimum Distance (worst-case pair) |
-
-### Per-Dataset Winners (by ILAD region)
+### Per-Dataset Best Configs (≥95% baseline nDCG)
 
-| Dataset | Low (0.3-0.5) | Moderate (0.5-0.7) | High (0.7-0.9) |
-|---------|:-------------:|:------------------:|:--------------:|
-| MovieLens-32M | DPP | DPP | MSD |
-| Last.FM | SSD | MSD | MSD |
-| Amazon-VG | - | - | MMR |
-| Goodreads | MSD | MSD | MSD |
+| Dataset | Max ILAD | Max ILMD | Best Overall |
+|---------|:--------:|:--------:|:------------:|
+| MovieLens-32M | DPP (λ=0.9) | DPP (λ=0.9) | DPP (λ=0.9) |
+| Last.FM | MSD (λ=0.6) | DPP (λ=0.8) | DPP (λ=0.8) |
+| Amazon-VG | MSD (λ=0.5) | DPP (λ=0.9) | DPP (λ=0.9) |
+| Goodreads | MSD (λ=0.5) | DPP (λ=0.8) | DPP (λ=0.8) |
 
 *Raw JSON results are in `results/*.json`*
 
@@ -170,23 +155,32 @@ python -m benchmarks report
 - **Selection**: k=20 items selected by each strategy
 - **diversity sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
-### How We Compute "Wins"
+### Relevance-Budgeted Evaluation
+
+We use a **relevance floor** approach to ensure fair comparison:
+
+1. **Baseline**: For each dataset, compute baseline nDCG at `diversity=0` (no diversification)
+2. **Filter**: Keep only configs where nDCG ≥ 95% of baseline (deployable configurations)
+3. **Compare**: Among feasible configs, find which strategy achieves:
+   - **Max ILAD**: Best overall diversity spread
+   - **Max ILMD**: Best worst-case diversity (no similar pairs)
+   - **Best Combined**: Geometric mean of normalized ILAD and ILMD gains
 
-For each diversity region (e.g., ILAD 0.5-0.7), we:
+The **Combined Score** normalizes gains per dataset:
+- `ILAD_gain = (ILAD - ILAD_min) / (ILAD_max - ILAD_min)`
+- `ILMD_gain = (ILMD - ILMD_min) / (ILMD_max - ILMD_min)`
+- `Combined = sqrt(ILAD_gain × ILMD_gain)`
 
-1. **Filter** each strategy's results to points within that region
-2. **Find max nDCG** achieved by each strategy in that region
-3. **Compare across strategies** per dataset—the strategy with highest max nDCG "wins" that dataset
-4. **Count wins** across all 4 datasets to determine the overall leader
+This ensures a strategy must improve *both* metrics to score well—high only if both improve.
 
-This approach measures: *"At a given diversity level, which strategy can achieve the best relevance?"*
+### Diversity Metrics
 
-Regions are computed dynamically by dividing the observed metric range (across all strategies and datasets) into thirds:
-- **Low**: First third of observed range
-- **Moderate**: Middle third
-- **High**: Top third
+| Metric | Type | Description |
+|--------|------|-------------|
+| **ILAD** | Average | Mean pairwise distance—measures overall variety |
+| **ILMD** | Minimum | Minimum pairwise distance—ensures no similar pairs |
 
-*Note: Not all strategies reach all regions on all datasets. A "-" in the per-dataset table means no data points in that region.*
+Both are computed as `1 - cosine_similarity` between item embeddings.
 
 <details>
 <summary>Programmatic API</summary>
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 7dc9b51..128fb11 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -10,91 +10,173 @@
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
-
-def _compute_dynamic_regions(all_data: list[dict], metric: str) -> dict[str, tuple[float, float]]:
-    """Compute diversity regions dynamically by dividing observed range into thirds."""
-    values = [p[metric] for p in all_data if metric in p]
-    if not values:
-        # Fallback defaults
-        if metric == "ilad":
-            return {"low": (0.45, 0.60), "moderate": (0.60, 0.75), "high": (0.75, 1.0)}
-        return {"low": (0.0, 0.20), "moderate": (0.20, 0.45), "high": (0.45, 0.75)}
-
-    min_val = min(values)
-    max_val = max(values)
-    step = (max_val - min_val) / 3
-
-    return {
-        "low": (min_val, min_val + step),
-        "moderate": (min_val + step, min_val + 2 * step),
-        "high": (min_val + 2 * step, max_val + 0.01),  # +0.01 to include max
-    }
+# Relevance floor: keep configs with at least this fraction of baseline nDCG
+RELEVANCE_FLOOR = 0.95
 
 
-def _compute_regional_winners(
+def _compute_relevance_budgeted_scores(
     all_data: list[dict],
-) -> tuple[dict[str, dict[str, dict[str, int]]], dict[str, dict[str, tuple[float, float]]]]:
+) -> dict[str, dict[str, dict[str, float | str]]]:
     """
-    Count dataset wins for each strategy in each diversity region.
+    Compute best configs per strategy under a relevance floor constraint.
 
-    A "win" means having the highest average nDCG for that region on a specific dataset.
-
-    Returns
-    -------
-        - results: dict mapping metric -> region -> strategy -> win_count
-        - ranges: dict mapping metric -> region -> (low, high) bounds
+    For each dataset, finds strategies that maximize ILAD, ILMD, and combined diversity
+    while maintaining >= 95% of baseline (lambda=0) nDCG.
 
+    Returns dict mapping dataset -> goal -> {strategy, lambda, ndcg, ilad, ilmd, score}
     """
-    results: dict[str, dict[str, dict[str, int]]] = {"ilad": {}, "ilmd": {}}
     datasets = sorted(set(p["dataset"] for p in all_data))
+    results: dict[str, dict[str, dict[str, float | str]]] = {}
 
-    # Compute dynamic regions based on actual data
-    ilad_regions = _compute_dynamic_regions(all_data, "ilad")
-    ilmd_regions = _compute_dynamic_regions(all_data, "ilmd")
-    all_regions = {"ilad": ilad_regions, "ilmd": ilmd_regions}
-
-    for metric, regions in [("ilad", ilad_regions), ("ilmd", ilmd_regions)]:
-        for region_name, (low, high) in regions.items():
-            win_counts: dict[str, int] = {s: 0 for s in STRATEGIES}
-
-            for dataset in datasets:
-                # Get points for this dataset in this region
-                region_points = [p for p in all_data if p["dataset"] == dataset and low <= p[metric] < high]
-                if not region_points:
-                    continue
-
-                # Compute avg nDCG per strategy for this dataset+region
-                strategy_ndcg: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-                for point in region_points:
-                    strategy_ndcg[point["strategy"]].append(point["ndcg"])
-
-                # Find winner for this dataset (using max nDCG - best achievable)
-                best_strategy = None
-                best_ndcg = -1.0
-                for strategy, ndcgs in strategy_ndcg.items():
-                    if ndcgs:
-                        max_ndcg = max(ndcgs)
-                        if max_ndcg > best_ndcg:
-                            best_ndcg = max_ndcg
-                            best_strategy = strategy
+    for dataset in datasets:
+        dataset_points = [p for p in all_data if p["dataset"] == dataset]
 
-                if best_strategy:
-                    win_counts[best_strategy] += 1
-
-            results[metric][region_name] = win_counts
-
-    return results, all_regions
-
-
-def _find_best_or_tied(wins: dict[str, int]) -> str:
-    """Find best strategy or list ties (e.g., 'DPP/MSD')."""
-    if not wins:
-        return "-"
-    max_wins = max(wins.values())
-    if max_wins == 0:
-        return "-"
-    winners = [s.upper() for s, count in wins.items() if count == max_wins]
-    return "/".join(sorted(winners))
+        # Find baseline nDCG (lambda=0, or minimum lambda for each strategy)
+        baseline_ndcg = 0.0
+        for strategy in STRATEGIES:
+            strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
+            if strategy_points:
+                min_lambda_point = min(strategy_points, key=lambda x: x["lambda"])
+                baseline_ndcg = max(baseline_ndcg, min_lambda_point["ndcg"])
+
+        if baseline_ndcg == 0:
+            continue
+
+        ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
+
+        # Find min/max for normalization
+        all_ilad = [p["ilad"] for p in dataset_points]
+        all_ilmd = [p["ilmd"] for p in dataset_points]
+        ilad_min, ilad_max = min(all_ilad), max(all_ilad)
+        ilmd_min, ilmd_max = min(all_ilmd), max(all_ilmd)
+
+        # Filter to feasible configs (above relevance floor)
+        feasible = [p for p in dataset_points if p["ndcg"] >= ndcg_floor]
+
+        if not feasible:
+            continue
+
+        results[dataset] = {}
+
+        # Goal 1: Max ILAD
+        best_ilad = max(feasible, key=lambda x: x["ilad"])
+        results[dataset]["max_ilad"] = {
+            "strategy": best_ilad["strategy"],
+            "lambda": best_ilad["lambda"],
+            "ndcg": best_ilad["ndcg"],
+            "ndcg_vs_baseline": best_ilad["ndcg"] / baseline_ndcg,
+            "ilad": best_ilad["ilad"],
+            "ilmd": best_ilad["ilmd"],
+        }
+
+        # Goal 2: Max ILMD
+        best_ilmd = max(feasible, key=lambda x: x["ilmd"])
+        results[dataset]["max_ilmd"] = {
+            "strategy": best_ilmd["strategy"],
+            "lambda": best_ilmd["lambda"],
+            "ndcg": best_ilmd["ndcg"],
+            "ndcg_vs_baseline": best_ilmd["ndcg"] / baseline_ndcg,
+            "ilad": best_ilmd["ilad"],
+            "ilmd": best_ilmd["ilmd"],
+        }
+
+        # Goal 3: Best combined (geometric mean of normalized gains)
+        def combined_score(point: dict) -> float:
+            ilad_range = ilad_max - ilad_min
+            ilmd_range = ilmd_max - ilmd_min
+            if ilad_range == 0 or ilmd_range == 0:
+                return 0.0
+            ilad_gain = (point["ilad"] - ilad_min) / ilad_range
+            ilmd_gain = (point["ilmd"] - ilmd_min) / ilmd_range
+            return (ilad_gain * ilmd_gain) ** 0.5  # Geometric mean
+
+        best_combined = max(feasible, key=combined_score)
+        results[dataset]["best_combined"] = {
+            "strategy": best_combined["strategy"],
+            "lambda": best_combined["lambda"],
+            "ndcg": best_combined["ndcg"],
+            "ndcg_vs_baseline": best_combined["ndcg"] / baseline_ndcg,
+            "ilad": best_combined["ilad"],
+            "ilmd": best_combined["ilmd"],
+            "score": combined_score(best_combined),
+        }
+
+    return results
+
+
+def _get_dataset_baseline_and_bounds(
+    dataset_points: list[dict],
+) -> tuple[float, float, float, float, float] | None:
+    """Get baseline nDCG and normalization bounds for a dataset."""
+    baseline_ndcg = 0.0
+    for strategy in STRATEGIES:
+        strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
+        if strategy_points:
+            min_lambda_point = min(strategy_points, key=lambda x: x["lambda"])
+            baseline_ndcg = max(baseline_ndcg, min_lambda_point["ndcg"])
+
+    if baseline_ndcg == 0:
+        return None
+
+    all_ilad = [p["ilad"] for p in dataset_points]
+    all_ilmd = [p["ilmd"] for p in dataset_points]
+    ilad_min, ilad_max = float(min(all_ilad)), float(max(all_ilad))
+    ilmd_min, ilmd_max = float(min(all_ilmd)), float(max(all_ilmd))
+
+    if ilad_max == ilad_min or ilmd_max == ilmd_min:
+        return None
+
+    return baseline_ndcg, ilad_min, ilad_max, ilmd_min, ilmd_max
+
+
+def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, float]]:
+    """Compute per-strategy aggregate scores across all datasets."""
+    datasets = sorted(set(p["dataset"] for p in all_data))
+    strategy_scores: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_lambdas: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ndcg_ratios: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+
+    for dataset in datasets:
+        dataset_points = [p for p in all_data if p["dataset"] == dataset]
+        bounds = _get_dataset_baseline_and_bounds(dataset_points)
+        if bounds is None:
+            continue
+
+        baseline_ndcg, ilad_min, ilad_max, ilmd_min, ilmd_max = bounds
+        ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
+        ilad_range = ilad_max - ilad_min
+        ilmd_range = ilmd_max - ilmd_min
+
+        def combined_score(point: dict) -> float:
+            ilad_gain = (point["ilad"] - ilad_min) / ilad_range
+            ilmd_gain = (point["ilmd"] - ilmd_min) / ilmd_range
+            return (ilad_gain * ilmd_gain) ** 0.5
+
+        for strategy in STRATEGIES:
+            strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
+            feasible = [p for p in strategy_points if p["ndcg"] >= ndcg_floor]
+            if not feasible:
+                continue
+
+            best_point = max(feasible, key=combined_score)
+            strategy_scores[strategy].append(combined_score(best_point))
+            strategy_lambdas[strategy].append(best_point["lambda"])
+            strategy_ndcg_ratios[strategy].append(best_point["ndcg"] / baseline_ndcg)
+
+    # Aggregate across datasets
+    scorecard: dict[str, dict[str, float]] = {}
+    for strategy in STRATEGIES:
+        if strategy_scores[strategy]:
+            scores = strategy_scores[strategy]
+            lambdas = strategy_lambdas[strategy]
+            ratios = strategy_ndcg_ratios[strategy]
+            scorecard[strategy] = {
+                "combined_score": sum(scores) / len(scores),
+                "avg_lambda": sum(lambdas) / len(lambdas),
+                "avg_ndcg_ratio": sum(ratios) / len(ratios),
+            }
+
+    return scorecard
 
 
 def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
@@ -200,57 +282,66 @@ def generate_latency_plot(output_path: Path) -> None:
     plt.close()
 
 
-def _log_regional_analysis(
-    regional: dict, ranges: dict[str, dict[str, tuple[float, float]]], num_datasets: int
-) -> None:
-    """Log regional analysis tables."""
-    logger.info(f"\nRegional Analysis (dataset wins out of {num_datasets} datasets):")
-    logger.info("\nILAD (Average Diversity):")
-    logger.info("| Region   | Range     | MMR | MSD | DPP | SSD | Best   |")
-    logger.info("|----------|-----------|-----|-----|-----|-----|--------|")
-    for region in ["low", "moderate", "high"]:
-        if region in regional["ilad"]:
-            wins = regional["ilad"][region]
-            low, high = ranges["ilad"][region]
-            best = _find_best_or_tied(wins)
-            logger.info(
-                f"| {region:8} | {low:.2f}-{high:.2f} | "
-                f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
-                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best:6} |"
-            )
+def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
+    """Log relevance-budgeted analysis tables."""
+    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{int(RELEVANCE_FLOOR*100)}% baseline nDCG) ===")
 
-    logger.info("\nILMD (Minimum Diversity):")
-    logger.info("| Region   | Range       | MMR | MSD | DPP | SSD | Best   |")
-    logger.info("|----------|-------------|-----|-----|-----|-----|--------|")
-    for region in ["low", "moderate", "high"]:
-        if region in regional["ilmd"]:
-            wins = regional["ilmd"][region]
-            low, high = ranges["ilmd"][region]
-            best = _find_best_or_tied(wins)
+    # Per-dataset recommendations
+    recommendations = _compute_relevance_budgeted_scores(all_data)
+
+    name_map = {
+        "ml-32m": "MovieLens-32M",
+        "lastfm": "Last.FM",
+        "amazon-product-reviews-video-games": "Amazon Video Games",
+        "goodreads-rating": "Goodreads",
+    }
+
+    logger.info("\nBest configs per dataset (maintaining ≥95% baseline relevance):")
+    logger.info("| Dataset | Goal | Strategy | λ | nDCG vs Base | ILAD | ILMD |")
+    logger.info("|---------|------|----------|---|--------------|------|------|")
+
+    for dataset, goals in recommendations.items():
+        display_name = name_map.get(dataset, dataset)[:16]
+        for goal, config in goals.items():
+            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+            strategy_name = str(config["strategy"]).upper()
             logger.info(
-                f"| {region:8} | {low:.2f}-{high:.2f} | "
-                f"{wins.get('mmr', 0):3d} | {wins.get('msd', 0):3d} | "
-                f"{wins.get('dpp', 0):3d} | {wins.get('ssd', 0):3d} | {best:6} |"
+                f"| {display_name:16} | {goal_display:12} | {strategy_name:8} | "
+                f"{config['lambda']:.1f} | {config['ndcg_vs_baseline']:.1%} | "
+                f"{config['ilad']:.3f} | {config['ilmd']:.3f} |"
             )
 
-    # Log the computed ranges
-    logger.info("\nDynamic ranges computed from data:")
-    logger.info(f"  ILAD: {ranges['ilad']}")
-    logger.info(f"  ILMD: {ranges['ilmd']}")
+    # Strategy scorecard
+    scorecard = _compute_strategy_scorecard(all_data)
+    logger.info("\n=== Strategy Scorecard (averaged across datasets) ===")
+    logger.info("| Strategy | Combined Score | Avg λ | Avg nDCG vs Base |")
+    logger.info("|----------|----------------|-------|------------------|")
+
+    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
+    for strategy, scores in sorted_strategies:
+        logger.info(
+            f"| {strategy.upper():8} | {scores['combined_score']:.3f}          | "
+            f"{scores['avg_lambda']:.2f}  | {scores['avg_ndcg_ratio']:.1%}            |"
+        )
+
+    # Summary
+    logger.info("\n=== Key Findings ===")
+    if sorted_strategies:
+        best_strategy = sorted_strategies[0][0].upper()
+        logger.info(f"Best overall (balanced ILAD+ILMD): {best_strategy}")
 
-    # Summary: total wins across all regions
-    logger.info("\nTotal Wins Summary:")
-    total_wins: dict[str, int] = {s: 0 for s in STRATEGIES}
-    for metric in ["ilad", "ilmd"]:
-        for region_wins in regional[metric].values():
-            for strategy, count in region_wins.items():
-                total_wins[strategy] += count
+    # Count wins per goal
+    goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
+    for goals in recommendations.values():
+        for goal, config in goals.items():
+            strategy_str = str(config["strategy"])
+            goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
 
-    sorted_strategies = sorted(total_wins.items(), key=lambda x: x[1], reverse=True)
-    logger.info("| Strategy | Total Wins |")
-    logger.info("|----------|------------|")
-    for strategy, count in sorted_strategies:
-        logger.info(f"| {strategy.upper():8} | {count:10d} |")
+    for goal, wins in goal_wins.items():
+        if wins:
+            winner = max(wins.items(), key=lambda x: x[1])
+            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+            logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
 
 
 def generate_report(results_dir: Path) -> None:
@@ -294,9 +385,7 @@ def generate_report(results_dir: Path) -> None:
     generate_latency_plot(latency_path)
     logger.debug(f"Saved: {latency_path}")
 
-    # Regional analysis - count wins per dataset
-    regional, ranges = _compute_regional_winners(all_data)
-    num_datasets = len(set(p["dataset"] for p in all_data))
-    _log_regional_analysis(regional, ranges, num_datasets)
+    # Relevance-budgeted analysis
+    _log_relevance_budgeted_analysis(all_data)
 
     logger.info(f"\nReport generated: {results_dir}")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From 91f6c645c0964c410f5e47e461f1002990b76ca0 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 15:14:59 +0100
Subject: [PATCH 20/69] Switched to budget relevance approach

---
 benchmarks/README.md | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1142319..8a3aa49 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -25,16 +25,16 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance**. This ensures we only compare "deployable" configurations.
+We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance**.
 
 ### Strategy Scorecard
 
-| Strategy | Combined Score | Typical λ | Avg nDCG vs Baseline |
-|----------|:--------------:|:---------:|:--------------------:|
-| **DPP**  | **0.439**      | 0.85      | 99.3%                |
-| SSD      | 0.236          | 0.85      | 101.9%               |
-| MSD      | 0.226          | 0.50      | 98.7%                |
-| MMR      | 0.213          | 0.78      | 101.4%               |
+| Strategy | Combined Score | Typical `diversity` | Avg nDCG vs Baseline |
+|----------|:--------------:|:-------------------:|:--------------------:|
+| **DPP**  | **0.439**      | 0.85                | 99%                  |
+| SSD      | 0.236          | 0.85                | 100%                 |
+| MSD      | 0.226          | 0.50                | 99%                  |
+| MMR      | 0.213          | 0.78                | 100%                 |
 
 *Combined Score = geometric mean of normalized ILAD and ILMD gains. Higher = better balance of both diversity metrics.*
 
@@ -127,10 +127,10 @@ python -m benchmarks report
 
 | Dataset | Max ILAD | Max ILMD | Best Overall |
 |---------|:--------:|:--------:|:------------:|
-| MovieLens-32M | DPP (λ=0.9) | DPP (λ=0.9) | DPP (λ=0.9) |
-| Last.FM | MSD (λ=0.6) | DPP (λ=0.8) | DPP (λ=0.8) |
-| Amazon-VG | MSD (λ=0.5) | DPP (λ=0.9) | DPP (λ=0.9) |
-| Goodreads | MSD (λ=0.5) | DPP (λ=0.8) | DPP (λ=0.8) |
+| MovieLens-32M | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
+| Last.FM | MSD (`diversity`=0.6) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
+| Amazon-VG | MSD (`diversity`=0.5) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
+| Goodreads | MSD (`diversity`=0.5) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 
 *Raw JSON results are in `results/*.json`*
 

From ed891f83448ee3d7bc2bd790c3c52175e220e122 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 15:18:57 +0100
Subject: [PATCH 21/69] Switched to budget relevance approach

---
 benchmarks/README.md           |  16 ++++++++--------
 benchmarks/core/report.py      |  22 +++++++++++-----------
 benchmarks/results/latency.png | Bin 77568 -> 79309 bytes
 3 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 8a3aa49..f1a947c 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -29,14 +29,14 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 
 ### Strategy Scorecard
 
-| Strategy | Combined Score | Typical `diversity` | Avg nDCG vs Baseline |
-|----------|:--------------:|:-------------------:|:--------------------:|
-| **DPP**  | **0.439**      | 0.85                | 99%                  |
-| SSD      | 0.236          | 0.85                | 100%                 |
-| MSD      | 0.226          | 0.50                | 99%                  |
-| MMR      | 0.213          | 0.78                | 100%                 |
-
-*Combined Score = geometric mean of normalized ILAD and ILMD gains. Higher = better balance of both diversity metrics.*
+| Strategy | Combined Score | Best ILAD | Best ILMD |
+|----------|:--------------:|:---------:|:---------:|
+| **DPP**  | **0.439**      | 0.75      | **0.26**  |
+| SSD      | 0.236          | 0.62      | 0.17      |
+| MSD      | 0.226          | **0.78**  | 0.15      |
+| MMR      | 0.213          | 0.58      | 0.18      |
+
+*Combined Score = geometric mean of normalized ILAD and ILMD gains. Best ILAD/ILMD = average across datasets while maintaining ≥95% baseline relevance.*
 
 ### Best Configs Per Goal (≥95% baseline nDCG)
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 128fb11..8795366 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -133,8 +133,8 @@ def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, flo
     """Compute per-strategy aggregate scores across all datasets."""
     datasets = sorted(set(p["dataset"] for p in all_data))
     strategy_scores: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_lambdas: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ndcg_ratios: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ilads: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ilmds: dict[str, list[float]] = {s: [] for s in STRATEGIES}
 
     for dataset in datasets:
         dataset_points = [p for p in all_data if p["dataset"] == dataset]
@@ -160,20 +160,20 @@ def combined_score(point: dict) -> float:
 
             best_point = max(feasible, key=combined_score)
             strategy_scores[strategy].append(combined_score(best_point))
-            strategy_lambdas[strategy].append(best_point["lambda"])
-            strategy_ndcg_ratios[strategy].append(best_point["ndcg"] / baseline_ndcg)
+            strategy_ilads[strategy].append(best_point["ilad"])
+            strategy_ilmds[strategy].append(best_point["ilmd"])
 
     # Aggregate across datasets
     scorecard: dict[str, dict[str, float]] = {}
     for strategy in STRATEGIES:
         if strategy_scores[strategy]:
             scores = strategy_scores[strategy]
-            lambdas = strategy_lambdas[strategy]
-            ratios = strategy_ndcg_ratios[strategy]
+            ilads = strategy_ilads[strategy]
+            ilmds = strategy_ilmds[strategy]
             scorecard[strategy] = {
                 "combined_score": sum(scores) / len(scores),
-                "avg_lambda": sum(lambdas) / len(lambdas),
-                "avg_ndcg_ratio": sum(ratios) / len(ratios),
+                "best_ilad": sum(ilads) / len(ilads),
+                "best_ilmd": sum(ilmds) / len(ilmds),
             }
 
     return scorecard
@@ -314,14 +314,14 @@ def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     # Strategy scorecard
     scorecard = _compute_strategy_scorecard(all_data)
     logger.info("\n=== Strategy Scorecard (averaged across datasets) ===")
-    logger.info("| Strategy | Combined Score | Avg λ | Avg nDCG vs Base |")
-    logger.info("|----------|----------------|-------|------------------|")
+    logger.info("| Strategy | Combined Score | Best ILAD | Best ILMD |")
+    logger.info("|----------|----------------|-----------|-----------|")
 
     sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
     for strategy, scores in sorted_strategies:
         logger.info(
             f"| {strategy.upper():8} | {scores['combined_score']:.3f}          | "
-            f"{scores['avg_lambda']:.2f}  | {scores['avg_ndcg_ratio']:.1%}            |"
+            f"{scores['best_ilad']:.2f}      | {scores['best_ilmd']:.2f}      |"
         )
 
     # Summary
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From 33700108ee6ff4573fc4a83666c1cbb8c6a702fc Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 15:55:05 +0100
Subject: [PATCH 22/69] Expanded results

---
 benchmarks/README.md | 65 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index f1a947c..2edd89b 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -132,6 +132,71 @@ python -m benchmarks report
 | Amazon-VG | MSD (`diversity`=0.5) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
 | Goodreads | MSD (`diversity`=0.5) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 
+</details>
+
+<details>
+<summary>Per-Dataset Detailed Metrics</summary>
+
+The tables below show best achievable metrics per strategy while maintaining ≥95% of baseline nDCG.
+
+#### MovieLens-32M
+
+| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
+|----------|:----------------:|:----:|:----:|:----:|:--------:|
+| **DPP**  | 0.9              | 0.052 | 0.67 | 0.25 | **0.46** |
+| SSD      | 0.8              | 0.054 | 0.58 | 0.18 | 0.32     |
+| MSD      | 0.5              | 0.055 | 0.64 | 0.10 | 0.25     |
+| MMR      | 0.7              | 0.056 | 0.54 | 0.14 | 0.27     |
+
+#### Last.FM
+
+| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
+|----------|:----------------:|:----:|:----:|:----:|:--------:|
+| **DPP**  | 0.8              | 0.151 | 0.73 | 0.19 | **0.37** |
+| SSD      | 0.9              | 0.152 | 0.55 | 0.12 | 0.18     |
+| MSD      | 0.6              | 0.154 | 0.84 | 0.10 | 0.22     |
+| MMR      | 0.8              | 0.155 | 0.57 | 0.15 | 0.19     |
+
+#### Amazon Video Games
+
+| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
+|----------|:----------------:|:----:|:----:|:----:|:--------:|
+| **DPP**  | 0.9              | 0.306 | 0.92 | 0.47 | **0.68** |
+| SSD      | 0.8              | 0.298 | 0.85 | 0.31 | 0.38     |
+| MSD      | 0.5              | 0.269 | 0.96 | 0.37 | 0.32     |
+| MMR      | 0.8              | 0.285 | 0.72 | 0.31 | 0.27     |
+
+#### Goodreads
+
+| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
+|----------|:----------------:|:----:|:----:|:----:|:--------:|
+| **DPP**  | 0.8              | 0.027 | 0.67 | 0.14 | **0.24** |
+| SSD      | 0.9              | 0.026 | 0.50 | 0.08 | 0.10     |
+| MSD      | 0.5              | 0.027 | 0.78 | 0.08 | 0.13     |
+| MMR      | 0.7              | 0.028 | 0.50 | 0.10 | 0.12     |
+
+*Combined = geometric mean of normalized ILAD and ILMD gains*
+
+</details>
+
+<details>
+<summary>How Combined Score is Computed</summary>
+
+For each dataset, we normalize ILAD and ILMD gains to [0, 1]:
+
+```
+ILAD_gain = (ILAD - ILAD_min) / (ILAD_max - ILAD_min)
+ILMD_gain = (ILMD - ILMD_min) / (ILMD_max - ILMD_min)
+Combined = sqrt(ILAD_gain × ILMD_gain)
+```
+
+The geometric mean ensures a strategy must improve *both* metrics to score well. The final Combined Score in the scorecard is the average across all 4 datasets.
+
+Example for DPP on Amazon Video Games:
+- ILAD_gain ≈ 0.83 (high overall variety)
+- ILMD_gain ≈ 0.55 (good worst-case diversity)
+- Combined = sqrt(0.83 × 0.55) ≈ 0.68
+
 *Raw JSON results are in `results/*.json`*
 
 </details>

From c21b8c781d331feebcbde066a49b475f1e110068 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:06:19 +0100
Subject: [PATCH 23/69] Updated results

---
 benchmarks/README.md | 48 +++++++++++---------------------------------
 1 file changed, 12 insertions(+), 36 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 2edd89b..0ba176d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -18,6 +18,7 @@ In addition, we measure latency of each strategy as the number of candidates sca
 - [Key Findings](#key-findings)
 - [Relevance-Diversity Tradeoff](#relevance-diversity-tradeoff)
 - [Latency](#latency)
+- [Detailed Results](#detailed-results)
 - [Usage](#usage)
 - [Datasets](#datasets)
 - [Methodology](#methodology)
@@ -25,9 +26,9 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance**.
+We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance** (see [Methodology](#methodology) for details).
 
-### Strategy Scorecard
+### Strategy Performance
 
 | Strategy | Combined Score | Best ILAD | Best ILMD |
 |----------|:--------------:|:---------:|:---------:|
@@ -36,15 +37,15 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 | MSD      | 0.226          | **0.78**  | 0.15      |
 | MMR      | 0.213          | 0.58      | 0.18      |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains. Best ILAD/ILMD = average across datasets while maintaining ≥95% baseline relevance.*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Best ILAD/ILMD = average across datasets while maintaining ≥95% baseline relevance.*
 
-### Best Configs Per Goal (≥95% baseline nDCG)
+### Summary
 
-| Goal | Winner | Wins | Why |
-|------|--------|:----:|-----|
-| **Best Overall** | **DPP** | 4/4 | Highest combined diversity (both ILAD and ILMD) |
-| **Max ILAD** (overall variety) | **MSD** | 3/4 | Optimizes sum of pairwise distances |
-| **Max ILMD** (no similar pairs) | **DPP** | 4/4 | Best worst-case diversity guarantee |
+| Goal | Winner | Why |
+|------|--------|-----|
+| **Best Overall** | **DPP** | Highest combined diversity (both ILAD and ILMD) |
+| **Max ILAD** (overall variety) | **MSD** | Optimizes sum of pairwise distances |
+| **Max ILMD** (no similar pairs) | **DPP** | Best worst-case diversity guarantee |
 
 ### Recommendations
 
@@ -111,8 +112,7 @@ python -m benchmarks run
 python -m benchmarks report
 ```
 
-<details>
-<summary>Detailed Results</summary>
+## Detailed Results
 
 ### Strategies
 
@@ -132,8 +132,6 @@ python -m benchmarks report
 | Amazon-VG | MSD (`diversity`=0.5) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
 | Goodreads | MSD (`diversity`=0.5) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 
-</details>
-
 <details>
 <summary>Per-Dataset Detailed Metrics</summary>
 
@@ -179,28 +177,6 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 
 </details>
 
-<details>
-<summary>How Combined Score is Computed</summary>
-
-For each dataset, we normalize ILAD and ILMD gains to [0, 1]:
-
-```
-ILAD_gain = (ILAD - ILAD_min) / (ILAD_max - ILAD_min)
-ILMD_gain = (ILMD - ILMD_min) / (ILMD_max - ILMD_min)
-Combined = sqrt(ILAD_gain × ILMD_gain)
-```
-
-The geometric mean ensures a strategy must improve *both* metrics to score well. The final Combined Score in the scorecard is the average across all 4 datasets.
-
-Example for DPP on Amazon Video Games:
-- ILAD_gain ≈ 0.83 (high overall variety)
-- ILMD_gain ≈ 0.55 (good worst-case diversity)
-- Combined = sqrt(0.83 × 0.55) ≈ 0.68
-
-*Raw JSON results are in `results/*.json`*
-
-</details>
-
 ## Datasets
 
 | Dataset | Domain | Interactions | Source |
@@ -225,7 +201,7 @@ Example for DPP on Amazon Video Games:
 We use a **relevance floor** approach to ensure fair comparison:
 
 1. **Baseline**: For each dataset, compute baseline nDCG at `diversity=0` (no diversification)
-2. **Filter**: Keep only configs where nDCG ≥ 95% of baseline (deployable configurations)
+2. **Filter**: Keep only configs where nDCG ≥ 95% of baseline to ensure relevance
 3. **Compare**: Among feasible configs, find which strategy achieves:
    - **Max ILAD**: Best overall diversity spread
    - **Max ILMD**: Best worst-case diversity (no similar pairs)

From 737d009d6ae541e99c22518f7274f5f0871ec833 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:10:38 +0100
Subject: [PATCH 24/69] Updated results

---
 benchmarks/README.md | 26 +++++++-------------------
 1 file changed, 7 insertions(+), 19 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 0ba176d..0c70879 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -28,7 +28,7 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance** (see [Methodology](#methodology) for details).
 
-### Strategy Performance
+### Overall Results
 
 | Strategy | Combined Score | Best ILAD | Best ILMD |
 |----------|:--------------:|:---------:|:---------:|
@@ -39,20 +39,14 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 
 *Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Best ILAD/ILMD = average across datasets while maintaining ≥95% baseline relevance.*
 
-### Summary
-
-| Goal | Winner | Why |
-|------|--------|-----|
-| **Best Overall** | **DPP** | Highest combined diversity (both ILAD and ILMD) |
-| **Max ILAD** (overall variety) | **MSD** | Optimizes sum of pairwise distances |
-| **Max ILMD** (no similar pairs) | **DPP** | Best worst-case diversity guarantee |
+**DPP wins overall** by balancing both ILAD (variety) and ILMD (no duplicates). MSD achieves highest ILAD but at the cost of ILMD.
 
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
 | **Best overall balance** | **DPP** | 0.8-0.9 | Wins both overall and ILMD |
-| **Maximum variety** | **MSD** | 0.5-0.6 | Best ILAD while keeping relevance |
+| **Maximum diversity** | **MSD** | 0.5-0.6 | Best ILAD while keeping relevance |
 | **Avoid similar pairs** | **DPP** | 0.8-0.9 | Strictest duplicate avoidance |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
 | **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
@@ -114,16 +108,8 @@ python -m benchmarks report
 
 ## Detailed Results
 
-### Strategies
-
-| Strategy | Description | Complexity |
-|----------|-------------|------------|
-| **MMR** | Maximal Marginal Relevance | O(k·n) |
-| **MSD** | Max-Sum Diversification | O(k·n) |
-| **DPP** | Determinantal Point Process | O(k²·n) |
-| **SSD** | Sliding Spectrum Decomposition | O(k²·n·d) |
-
-### Per-Dataset Best Configs (≥95% baseline nDCG)
+<details>
+<summary>Per-Dataset Best Configs (≥95% baseline nDCG)</summary>
 
 | Dataset | Max ILAD | Max ILMD | Best Overall |
 |---------|:--------:|:--------:|:------------:|
@@ -132,6 +118,8 @@ python -m benchmarks report
 | Amazon-VG | MSD (`diversity`=0.5) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
 | Goodreads | MSD (`diversity`=0.5) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 
+</details>
+
 <details>
 <summary>Per-Dataset Detailed Metrics</summary>
 

From fd0c1617b67622f38a9399ee1f00eb204a03321e Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:12:04 +0100
Subject: [PATCH 25/69] Updated results

---
 benchmarks/README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 0c70879..1b61374 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -161,8 +161,6 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 | MSD      | 0.5              | 0.027 | 0.78 | 0.08 | 0.13     |
 | MMR      | 0.7              | 0.028 | 0.50 | 0.10 | 0.12     |
 
-*Combined = geometric mean of normalized ILAD and ILMD gains*
-
 </details>
 
 ## Datasets

From 3962a3a383547a5bdf6b30a5ed1ff6a1aa5eff6a Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:23:38 +0100
Subject: [PATCH 26/69] Updated plots

---
 benchmarks/core/report.py          |  38 +++++++++++++++--------------
 benchmarks/results/latency.png     | Bin 79309 -> 72555 bytes
 benchmarks/results/pareto_ilad.png | Bin 298105 -> 275982 bytes
 benchmarks/results/pareto_ilmd.png | Bin 311456 -> 288769 bytes
 4 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 8795366..8b2a2d4 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -193,18 +193,21 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
     name_map = {
         "ml-32m": "MovieLens-32M",
         "lastfm": "Last.FM",
-        "amazon-product-reviews-video-games": "Amazon Video Games",
-        "goodreads-rating": "Goodreads",
+        "amazon-video-games": "Amazon Video Games",
+        "goodreads": "Goodreads",
     }
 
     metric_label = "ILAD (Avg Diversity)" if diversity_metric == "ilad" else "ILMD (Min Diversity)"
 
-    for ax, dataset in zip(axes, datasets):
-        ax.axvspan(0.3, 0.5, alpha=0.1, color="blue", label="_Low")
-        ax.axvspan(0.5, 0.7, alpha=0.1, color="green", label="_Moderate")
-        ax.axvspan(0.7, 0.9, alpha=0.1, color="orange", label="_High")
-        ax.axvspan(0.9, 1.0, alpha=0.1, color="red", label="_Max")
+    # Use seaborn styling
+    try:
+        import seaborn as sns
+
+        sns.set_theme(style="whitegrid", palette="muted")
+    except ImportError:
+        pass  # Fall back to matplotlib defaults
 
+    for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
             strategy_points = [p for p in all_data if p["dataset"] == dataset and p["strategy"] == strategy]
             strategy_points = sorted(strategy_points, key=lambda x: x["lambda"])
@@ -223,13 +226,6 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
         ax.grid(True, alpha=0.3, linestyle="--")
         ax.set_xlim(0.0, 1.05)
 
-    first_ax = axes[0]
-    y_top = first_ax.get_ylim()[1]
-    first_ax.text(0.4, y_top * 0.95, "Low", ha="center", fontsize=8, color="blue", alpha=0.7)
-    first_ax.text(0.6, y_top * 0.95, "Med", ha="center", fontsize=8, color="green", alpha=0.7)
-    first_ax.text(0.8, y_top * 0.95, "High", ha="center", fontsize=8, color="orange", alpha=0.7)
-    first_ax.text(0.95, y_top * 0.95, "Max", ha="center", fontsize=8, color="red", alpha=0.7)
-
     title_suffix = "(Average Diversity)" if diversity_metric == "ilad" else "(Minimum Diversity)"
     plt.suptitle(f"Relevance vs Diversity Tradeoff {title_suffix}", fontsize=14, fontweight="bold", y=1.02)
     plt.tight_layout()
@@ -243,6 +239,14 @@ def generate_latency_plot(output_path: Path) -> None:
 
     from benchmarks.core.latency import run_latency_benchmark
 
+    # Use seaborn styling
+    try:
+        import seaborn as sns
+
+        sns.set_theme(style="whitegrid", palette="muted")
+    except ImportError:
+        pass  # Fall back to matplotlib defaults
+
     logger.info("Running synthetic latency benchmark...")
     results = run_latency_benchmark()
 
@@ -250,7 +254,6 @@ def generate_latency_plot(output_path: Path) -> None:
 
     strategies = ["mmr", "msd", "dpp", "ssd"]
     colors = {"mmr": "#e74c3c", "msd": "#2ecc71", "dpp": "#3498db", "ssd": "#9b59b6"}
-    linestyles = {"mmr": "-", "msd": "--", "dpp": "-.", "ssd": "-"}
     markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
 
     for strategy in strategies:
@@ -264,7 +267,6 @@ def generate_latency_plot(output_path: Path) -> None:
             x_vals,
             y_vals,
             color=colors[strategy],
-            linestyle=linestyles[strategy],
             marker=markers[strategy],
             label=strategy.upper(),
             markersize=8,
@@ -292,8 +294,8 @@ def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     name_map = {
         "ml-32m": "MovieLens-32M",
         "lastfm": "Last.FM",
-        "amazon-product-reviews-video-games": "Amazon Video Games",
-        "goodreads-rating": "Goodreads",
+        "amazon-video-games": "Amazon Video Games",
+        "goodreads": "Goodreads",
     }
 
     logger.info("\nBest configs per dataset (maintaining ≥95% baseline relevance):")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index 394d7c3f1cf2b1cb82deba705d2d74b32469a5b6..4578f1df98fc4829165cb01de861c5a633e60740 100644
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From ec351e42daed77f78a3250c3c2fca6f76c6ec1af Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:29:21 +0100
Subject: [PATCH 27/69] Updated plots

---
 benchmarks/core/report.py | 26 +++++++++++---------------
 1 file changed, 11 insertions(+), 15 deletions(-)

diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 8b2a2d4..d356b2e 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -8,6 +8,14 @@
 
 logger = logging.getLogger(__name__)
 
+
+def _setup_plot_style() -> None:
+    """Set up seaborn styling for plots."""
+    import seaborn as sns
+
+    sns.set_theme(style="whitegrid", palette="muted")
+
+
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
 # Relevance floor: keep configs with at least this fraction of baseline nDCG
@@ -183,6 +191,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
     """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
     import matplotlib.pyplot as plt
 
+    _setup_plot_style()
+
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
     axes = axes.flatten()
 
@@ -199,14 +209,6 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
     metric_label = "ILAD (Avg Diversity)" if diversity_metric == "ilad" else "ILMD (Min Diversity)"
 
-    # Use seaborn styling
-    try:
-        import seaborn as sns
-
-        sns.set_theme(style="whitegrid", palette="muted")
-    except ImportError:
-        pass  # Fall back to matplotlib defaults
-
     for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
             strategy_points = [p for p in all_data if p["dataset"] == dataset and p["strategy"] == strategy]
@@ -239,13 +241,7 @@ def generate_latency_plot(output_path: Path) -> None:
 
     from benchmarks.core.latency import run_latency_benchmark
 
-    # Use seaborn styling
-    try:
-        import seaborn as sns
-
-        sns.set_theme(style="whitegrid", palette="muted")
-    except ImportError:
-        pass  # Fall back to matplotlib defaults
+    _setup_plot_style()
 
     logger.info("Running synthetic latency benchmark...")
     results = run_latency_benchmark()

From 8030d90a3b7e7cdb0af4e33be06ab31b75aa2c5f Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:35:07 +0100
Subject: [PATCH 28/69] Updated plots

---
 benchmarks/core/report.py          |  11 ++++++++++-
 benchmarks/results/latency.png     | Bin 72555 -> 79730 bytes
 benchmarks/results/pareto_ilad.png | Bin 275982 -> 306680 bytes
 benchmarks/results/pareto_ilmd.png | Bin 288769 -> 316272 bytes
 4 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index d356b2e..2def6bd 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -209,6 +209,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
     metric_label = "ILAD (Avg Diversity)" if diversity_metric == "ilad" else "ILMD (Min Diversity)"
 
+    markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
+
     for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
             strategy_points = [p for p in all_data if p["dataset"] == dataset and p["strategy"] == strategy]
@@ -218,7 +220,14 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
                 x_vals = [p[diversity_metric] for p in strategy_points]
                 y_vals = [p["ndcg"] for p in strategy_points]
                 ax.plot(
-                    x_vals, y_vals, "o-", color=colors[strategy], label=strategy.upper(), markersize=7, linewidth=2.5
+                    x_vals,
+                    y_vals,
+                    color=colors[strategy],
+                    marker=markers[strategy],
+                    label=strategy.upper(),
+                    markersize=7,
+                    linewidth=2.5,
+                    alpha=0.8,
                 )
 
         ax.set_xlabel(f"{metric_label} →", fontsize=10)
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index 4578f1df98fc4829165cb01de861c5a633e60740..e81cd6fb1177f4d6b9a8229e484645d522bd743c 100644
GIT binary patch
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From 74f416fd39e74101f28f8ac0b5b1a59254a1b2c0 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:37:01 +0100
Subject: [PATCH 29/69] Updated plots

---
 benchmarks/core/report.py          |   4 +++-
 benchmarks/results/latency.png     | Bin 79730 -> 74373 bytes
 benchmarks/results/pareto_ilad.png | Bin 306680 -> 306501 bytes
 benchmarks/results/pareto_ilmd.png | Bin 316272 -> 316953 bytes
 4 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 2def6bd..170f21d 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -233,7 +233,9 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
         ax.set_xlabel(f"{metric_label} →", fontsize=10)
         ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
         ax.set_title(name_map.get(dataset, dataset), fontsize=12, fontweight="bold")
-        ax.legend(loc="upper right", fontsize=9, framealpha=0.9)
+        # ILAD data is mostly on the right, ILMD data more spread out
+        legend_loc = "upper left" if diversity_metric == "ilad" else "upper right"
+        ax.legend(loc=legend_loc, fontsize=9, framealpha=0.9)
         ax.grid(True, alpha=0.3, linestyle="--")
         ax.set_xlim(0.0, 1.05)
 
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index e81cd6fb1177f4d6b9a8229e484645d522bd743c..fa31972c68cba59a6bdc90c3dd93984c35e83091 100644
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BGywnr


From feadfe13e8dd5d5f9d1efd57350b8b0f6bbc0cc3 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:41:23 +0100
Subject: [PATCH 30/69] Updated plots

---
 benchmarks/core/report.py          |   7 +++++--
 benchmarks/results/latency.png     | Bin 74373 -> 78806 bytes
 benchmarks/results/pareto_ilad.png | Bin 306501 -> 309414 bytes
 benchmarks/results/pareto_ilmd.png | Bin 316953 -> 319234 bytes
 4 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 170f21d..7012ef6 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -239,8 +239,11 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
         ax.grid(True, alpha=0.3, linestyle="--")
         ax.set_xlim(0.0, 1.05)
 
-    title_suffix = "(Average Diversity)" if diversity_metric == "ilad" else "(Minimum Diversity)"
-    plt.suptitle(f"Relevance vs Diversity Tradeoff {title_suffix}", fontsize=14, fontweight="bold", y=1.02)
+    if diversity_metric == "ilad":
+        title = "Relevance vs Average Pairwise Diversity (nDCG@10 vs ILAD)"
+    else:
+        title = "Relevance vs Minimum Pairwise Diversity (nDCG@10 vs ILMD)"
+    plt.suptitle(title, fontsize=14, fontweight="bold", y=1.02)
     plt.tight_layout()
     fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white")
     plt.close()
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index fa31972c68cba59a6bdc90c3dd93984c35e83091..e096baa806bfb3026b3c65e9ba50ca8abf231ae9 100644
GIT binary patch
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From 391684eaa2a29928d8f2ee1d02570b767c836253 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:47:17 +0100
Subject: [PATCH 31/69] Updated results

---
 benchmarks/README.md           |  26 ++++-----
 benchmarks/core/report.py      | 100 +++++++++++++++++++++------------
 benchmarks/results/latency.png | Bin 78806 -> 76956 bytes
 3 files changed, 78 insertions(+), 48 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1b61374..9f44190 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -30,14 +30,14 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 
 ### Overall Results
 
-| Strategy | Combined Score | Best ILAD | Best ILMD |
-|----------|:--------------:|:---------:|:---------:|
-| **DPP**  | **0.439**      | 0.75      | **0.26**  |
-| SSD      | 0.236          | 0.62      | 0.17      |
-| MSD      | 0.226          | **0.78**  | 0.15      |
-| MMR      | 0.213          | 0.58      | 0.18      |
+| Strategy | Combined Score | ILAD | ILMD |
+|----------|:--------------:|:----:|:----:|
+| **DPP**  | **0.439**      | 0.75 | **0.26** |
+| SSD      | 0.236          | 0.62 | 0.17 |
+| MSD      | 0.226          | **0.78** | 0.15 |
+| MMR      | 0.213          | 0.58 | 0.18 |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Best ILAD/ILMD = average across datasets while maintaining ≥95% baseline relevance.*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains relative to baseline (higher = better). ILAD/ILMD = values at best operating point, averaged across datasets while maintaining ≥95% baseline relevance.*
 
 **DPP wins overall** by balancing both ILAD (variety) and ILMD (no duplicates). MSD achieves highest ILAD but at the cost of ILMD.
 
@@ -47,7 +47,7 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 |------|----------|:-----------:|-------|
 | **Best overall balance** | **DPP** | 0.8-0.9 | Wins both overall and ILMD |
 | **Maximum diversity** | **MSD** | 0.5-0.6 | Best ILAD while keeping relevance |
-| **Avoid similar pairs** | **DPP** | 0.8-0.9 | Strictest duplicate avoidance |
+| **Avoid similar pairs** | **DPP** | 0.8-0.9 | Best at preventing near-duplicates |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
 | **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
 
@@ -176,7 +176,7 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 
 ### Experimental Setup
 
-- **Evaluation**: Leave-one-out protocol with 2,000 sampled users per dataset
+- **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=20 items selected by each strategy
@@ -193,9 +193,9 @@ We use a **relevance floor** approach to ensure fair comparison:
    - **Max ILMD**: Best worst-case diversity (no similar pairs)
    - **Best Combined**: Geometric mean of normalized ILAD and ILMD gains
 
-The **Combined Score** normalizes gains per dataset:
-- `ILAD_gain = (ILAD - ILAD_min) / (ILAD_max - ILAD_min)`
-- `ILMD_gain = (ILMD - ILMD_min) / (ILMD_max - ILMD_min)`
+The **Combined Score** normalizes gains relative to baseline (λ=0):
+- `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`
+- `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_max - ILMD_baseline)`
 - `Combined = sqrt(ILAD_gain × ILMD_gain)`
 
 This ensures a strategy must improve *both* metrics to score well—high only if both improve.
@@ -205,7 +205,7 @@ This ensures a strategy must improve *both* metrics to score well—high only if
 | Metric | Type | Description |
 |--------|------|-------------|
 | **ILAD** | Average | Mean pairwise distance—measures overall variety |
-| **ILMD** | Minimum | Minimum pairwise distance—ensures no similar pairs |
+| **ILMD** | Minimum | Min pairwise distance—best at preventing near-duplicates |
 
 Both are computed as `1 - cosine_similarity` between item embeddings.
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 7012ef6..0b797e3 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -52,11 +52,19 @@ def _compute_relevance_budgeted_scores(
 
         ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
 
-        # Find min/max for normalization
-        all_ilad = [p["ilad"] for p in dataset_points]
-        all_ilmd = [p["ilmd"] for p in dataset_points]
-        ilad_min, ilad_max = min(all_ilad), max(all_ilad)
-        ilmd_min, ilmd_max = min(all_ilmd), max(all_ilmd)
+        # Find baseline (lambda=0) and max for normalization
+        ilad_baseline = (
+            min(p["ilad"] for p in dataset_points if p["lambda"] == 0.0)
+            if any(p["lambda"] == 0.0 for p in dataset_points)
+            else min(p["ilad"] for p in dataset_points)
+        )
+        ilmd_baseline = (
+            min(p["ilmd"] for p in dataset_points if p["lambda"] == 0.0)
+            if any(p["lambda"] == 0.0 for p in dataset_points)
+            else min(p["ilmd"] for p in dataset_points)
+        )
+        ilad_max = max(p["ilad"] for p in dataset_points)
+        ilmd_max = max(p["ilmd"] for p in dataset_points)
 
         # Filter to feasible configs (above relevance floor)
         feasible = [p for p in dataset_points if p["ndcg"] >= ndcg_floor]
@@ -66,8 +74,8 @@ def _compute_relevance_budgeted_scores(
 
         results[dataset] = {}
 
-        # Goal 1: Max ILAD
-        best_ilad = max(feasible, key=lambda x: x["ilad"])
+        # Goal 1: Max ILAD (tie-break by higher nDCG, lower lambda)
+        best_ilad = max(feasible, key=lambda x: (x["ilad"], x["ndcg"], -x["lambda"]))
         results[dataset]["max_ilad"] = {
             "strategy": best_ilad["strategy"],
             "lambda": best_ilad["lambda"],
@@ -77,8 +85,8 @@ def _compute_relevance_budgeted_scores(
             "ilmd": best_ilad["ilmd"],
         }
 
-        # Goal 2: Max ILMD
-        best_ilmd = max(feasible, key=lambda x: x["ilmd"])
+        # Goal 2: Max ILMD (tie-break by higher nDCG, lower lambda)
+        best_ilmd = max(feasible, key=lambda x: (x["ilmd"], x["ndcg"], -x["lambda"]))
         results[dataset]["max_ilmd"] = {
             "strategy": best_ilmd["strategy"],
             "lambda": best_ilmd["lambda"],
@@ -88,17 +96,18 @@ def _compute_relevance_budgeted_scores(
             "ilmd": best_ilmd["ilmd"],
         }
 
-        # Goal 3: Best combined (geometric mean of normalized gains)
+        # Goal 3: Best combined (geometric mean of normalized gains relative to baseline)
         def combined_score(point: dict) -> float:
-            ilad_range = ilad_max - ilad_min
-            ilmd_range = ilmd_max - ilmd_min
+            ilad_range = ilad_max - ilad_baseline
+            ilmd_range = ilmd_max - ilmd_baseline
             if ilad_range == 0 or ilmd_range == 0:
                 return 0.0
-            ilad_gain = (point["ilad"] - ilad_min) / ilad_range
-            ilmd_gain = (point["ilmd"] - ilmd_min) / ilmd_range
+            ilad_gain = (point["ilad"] - ilad_baseline) / ilad_range
+            ilmd_gain = (point["ilmd"] - ilmd_baseline) / ilmd_range
             return (ilad_gain * ilmd_gain) ** 0.5  # Geometric mean
 
-        best_combined = max(feasible, key=combined_score)
+        # Tie-break by higher nDCG, lower lambda
+        best_combined = max(feasible, key=lambda p: (combined_score(p), p["ndcg"], -p["lambda"]))
         results[dataset]["best_combined"] = {
             "strategy": best_combined["strategy"],
             "lambda": best_combined["lambda"],
@@ -115,7 +124,7 @@ def combined_score(point: dict) -> float:
 def _get_dataset_baseline_and_bounds(
     dataset_points: list[dict],
 ) -> tuple[float, float, float, float, float] | None:
-    """Get baseline nDCG and normalization bounds for a dataset."""
+    """Get baseline nDCG and normalization bounds (baseline, not min) for a dataset."""
     baseline_ndcg = 0.0
     for strategy in STRATEGIES:
         strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
@@ -126,23 +135,36 @@ def _get_dataset_baseline_and_bounds(
     if baseline_ndcg == 0:
         return None
 
-    all_ilad = [p["ilad"] for p in dataset_points]
-    all_ilmd = [p["ilmd"] for p in dataset_points]
-    ilad_min, ilad_max = float(min(all_ilad)), float(max(all_ilad))
-    ilmd_min, ilmd_max = float(min(all_ilmd)), float(max(all_ilmd))
+    # Use baseline (lambda=0) values, not min
+    has_baseline = any(p["lambda"] == 0.0 for p in dataset_points)
+    if has_baseline:
+        ilad_baseline = float(min(p["ilad"] for p in dataset_points if p["lambda"] == 0.0))
+        ilmd_baseline = float(min(p["ilmd"] for p in dataset_points if p["lambda"] == 0.0))
+    else:
+        ilad_baseline = float(min(p["ilad"] for p in dataset_points))
+        ilmd_baseline = float(min(p["ilmd"] for p in dataset_points))
+
+    ilad_max = float(max(p["ilad"] for p in dataset_points))
+    ilmd_max = float(max(p["ilmd"] for p in dataset_points))
 
-    if ilad_max == ilad_min or ilmd_max == ilmd_min:
+    if ilad_max == ilad_baseline or ilmd_max == ilmd_baseline:
         return None
 
-    return baseline_ndcg, ilad_min, ilad_max, ilmd_min, ilmd_max
+    return baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max
 
 
 def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, float]]:
-    """Compute per-strategy aggregate scores across all datasets."""
+    """
+    Compute per-strategy aggregate scores across all datasets.
+
+    Returns combined score, ILAD/ILMD at best operating point, nDCG retention, and typical diversity.
+    """
     datasets = sorted(set(p["dataset"] for p in all_data))
     strategy_scores: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_ilads: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_ilmds: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ndcg_ret: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_lambdas: dict[str, list[float]] = {s: [] for s in STRATEGIES}
 
     for dataset in datasets:
         dataset_points = [p for p in all_data if p["dataset"] == dataset]
@@ -150,14 +172,14 @@ def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, flo
         if bounds is None:
             continue
 
-        baseline_ndcg, ilad_min, ilad_max, ilmd_min, ilmd_max = bounds
+        baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max = bounds
         ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
-        ilad_range = ilad_max - ilad_min
-        ilmd_range = ilmd_max - ilmd_min
+        ilad_range = ilad_max - ilad_baseline
+        ilmd_range = ilmd_max - ilmd_baseline
 
         def combined_score(point: dict) -> float:
-            ilad_gain = (point["ilad"] - ilad_min) / ilad_range
-            ilmd_gain = (point["ilmd"] - ilmd_min) / ilmd_range
+            ilad_gain = (point["ilad"] - ilad_baseline) / ilad_range
+            ilmd_gain = (point["ilmd"] - ilmd_baseline) / ilmd_range
             return (ilad_gain * ilmd_gain) ** 0.5
 
         for strategy in STRATEGIES:
@@ -166,10 +188,13 @@ def combined_score(point: dict) -> float:
             if not feasible:
                 continue
 
-            best_point = max(feasible, key=combined_score)
+            # Select best point with tie-breaker
+            best_point = max(feasible, key=lambda p: (combined_score(p), p["ndcg"], -p["lambda"]))
             strategy_scores[strategy].append(combined_score(best_point))
             strategy_ilads[strategy].append(best_point["ilad"])
             strategy_ilmds[strategy].append(best_point["ilmd"])
+            strategy_ndcg_ret[strategy].append(best_point["ndcg"] / baseline_ndcg)
+            strategy_lambdas[strategy].append(best_point["lambda"])
 
     # Aggregate across datasets
     scorecard: dict[str, dict[str, float]] = {}
@@ -178,10 +203,14 @@ def combined_score(point: dict) -> float:
             scores = strategy_scores[strategy]
             ilads = strategy_ilads[strategy]
             ilmds = strategy_ilmds[strategy]
+            ndcg_rets = strategy_ndcg_ret[strategy]
+            lambdas = strategy_lambdas[strategy]
             scorecard[strategy] = {
                 "combined_score": sum(scores) / len(scores),
-                "best_ilad": sum(ilads) / len(ilads),
-                "best_ilmd": sum(ilmds) / len(ilmds),
+                "ilad_at_best": sum(ilads) / len(ilads),
+                "ilmd_at_best": sum(ilmds) / len(ilmds),
+                "ndcg_retention": sum(ndcg_rets) / len(ndcg_rets),
+                "typical_diversity": sum(lambdas) / len(lambdas),
             }
 
     return scorecard
@@ -326,14 +355,15 @@ def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     # Strategy scorecard
     scorecard = _compute_strategy_scorecard(all_data)
     logger.info("\n=== Strategy Scorecard (averaged across datasets) ===")
-    logger.info("| Strategy | Combined Score | Best ILAD | Best ILMD |")
-    logger.info("|----------|----------------|-----------|-----------|")
+    logger.info("| Strategy | Combined | ILAD | ILMD | nDCG Ret | Typical diversity |")
+    logger.info("|----------|----------|------|------|----------|-------------------|")
 
     sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
     for strategy, scores in sorted_strategies:
         logger.info(
-            f"| {strategy.upper():8} | {scores['combined_score']:.3f}          | "
-            f"{scores['best_ilad']:.2f}      | {scores['best_ilmd']:.2f}      |"
+            f"| {strategy.upper():8} | {scores['combined_score']:.3f}    | "
+            f"{scores['ilad_at_best']:.2f} | {scores['ilmd_at_best']:.2f} | "
+            f"{scores['ndcg_retention']:.1%}    | {scores['typical_diversity']:.1f}               |"
         )
 
     # Summary
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index e096baa806bfb3026b3c65e9ba50ca8abf231ae9..b3fd2dc3c31bf2d85353d7f79cb93581077a73fa 100644
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From 9dc73dc192c73c5783439846078503749adf64e1 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:54:40 +0100
Subject: [PATCH 32/69] Made code more consistent

---
 benchmarks/__init__.py        |  2 --
 benchmarks/__main__.py        | 10 ----------
 benchmarks/core/__init__.py   |  2 --
 benchmarks/core/config.py     |  2 --
 benchmarks/core/data.py       | 17 +----------------
 benchmarks/core/embeddings.py |  2 --
 benchmarks/core/latency.py    |  2 --
 benchmarks/core/metrics.py    |  2 --
 benchmarks/core/report.py     | 23 ++++++-----------------
 benchmarks/core/runner.py     | 15 +--------------
 10 files changed, 8 insertions(+), 69 deletions(-)

diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py
index 2413af1..d430eeb 100644
--- a/benchmarks/__init__.py
+++ b/benchmarks/__init__.py
@@ -1,5 +1,3 @@
-"""Pyversity benchmarking suite."""
-
 from benchmarks.core import BenchmarkConfig, run_benchmark
 
 __all__ = ["BenchmarkConfig", "run_benchmark"]
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index e9a339e..f102db9 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -1,13 +1,3 @@
-"""
-CLI entry point for benchmarks.
-
-Usage:
-    python -m benchmarks download   # Download datasets
-    python -m benchmarks run        # Run benchmarks on all datasets
-    python -m benchmarks report     # Generate markdown report + plots
-    python -m benchmarks            # Run all (default)
-"""
-
 from __future__ import annotations
 
 import argparse
diff --git a/benchmarks/core/__init__.py b/benchmarks/core/__init__.py
index cc462f9..702b470 100644
--- a/benchmarks/core/__init__.py
+++ b/benchmarks/core/__init__.py
@@ -1,5 +1,3 @@
-"""Core benchmark modules."""
-
 from benchmarks.core.config import BenchmarkConfig
 from benchmarks.core.data import DATASET_REGISTRY, DatasetInfo, DatasetType, InteractionData, load_dataset
 from benchmarks.core.latency import run_latency_benchmark
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index 1ff9146..aec0228 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -1,5 +1,3 @@
-"""Benchmark configuration dataclass with defaults for all parameters."""
-
 from dataclasses import dataclass, field
 from pathlib import Path
 
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
index 1b0a0e1..7734f34 100644
--- a/benchmarks/core/data.py
+++ b/benchmarks/core/data.py
@@ -1,5 +1,3 @@
-"""Dataset loading, preprocessing, and registry for benchmark evaluation."""
-
 from __future__ import annotations
 
 import logging
@@ -79,20 +77,7 @@ def load_dataset(
     min_interactions: int = 5,
     rating_threshold: float | None = None,
 ) -> InteractionData:
-    """
-    Load and preprocess a dataset.
-
-    Args:
-    ----
-        dataset: Dataset name (from registry) or DatasetInfo object
-        min_interactions: Minimum interactions per user AND item
-        rating_threshold: Override rating threshold (None = use dataset default)
-
-    Returns:
-    -------
-        Preprocessed InteractionData
-
-    """
+    """Load and preprocess a dataset from the registry."""
     # Resolve dataset info
     if isinstance(dataset, str):
         if dataset not in DATASET_REGISTRY:
diff --git a/benchmarks/core/embeddings.py b/benchmarks/core/embeddings.py
index f98196e..f68a920 100644
--- a/benchmarks/core/embeddings.py
+++ b/benchmarks/core/embeddings.py
@@ -1,5 +1,3 @@
-"""Item embedding generation via SVD and similarity matrix computation."""
-
 import logging
 
 import numpy as np
diff --git a/benchmarks/core/latency.py b/benchmarks/core/latency.py
index 415404d..cb31707 100644
--- a/benchmarks/core/latency.py
+++ b/benchmarks/core/latency.py
@@ -1,5 +1,3 @@
-"""Synthetic latency benchmark to measure algorithmic complexity."""
-
 from __future__ import annotations
 
 import time
diff --git a/benchmarks/core/metrics.py b/benchmarks/core/metrics.py
index abfb825..49414bd 100644
--- a/benchmarks/core/metrics.py
+++ b/benchmarks/core/metrics.py
@@ -1,5 +1,3 @@
-"""Relevance and diversity metrics for recommendation evaluation."""
-
 import numpy as np
 from numpy.typing import NDArray
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 0b797e3..50c9a17 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -1,19 +1,16 @@
-"""Plot generation from benchmark results."""
-
 from __future__ import annotations
 
 import json
 import logging
 from pathlib import Path
 
-logger = logging.getLogger(__name__)
-
+import matplotlib.pyplot as plt
+import seaborn as sns
 
-def _setup_plot_style() -> None:
-    """Set up seaborn styling for plots."""
-    import seaborn as sns
+logger = logging.getLogger(__name__)
 
-    sns.set_theme(style="whitegrid", palette="muted")
+# Set up seaborn styling
+sns.set_theme(style="whitegrid", palette="muted")
 
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
@@ -218,10 +215,6 @@ def combined_score(point: dict) -> float:
 
 def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
     """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
-    import matplotlib.pyplot as plt
-
-    _setup_plot_style()
-
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
     axes = axes.flatten()
 
@@ -280,12 +273,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
 def generate_latency_plot(output_path: Path) -> None:
     """Generate latency scaling plot using synthetic benchmark."""
-    import matplotlib.pyplot as plt
-
     from benchmarks.core.latency import run_latency_benchmark
 
-    _setup_plot_style()
-
     logger.info("Running synthetic latency benchmark...")
     results = run_latency_benchmark()
 
@@ -325,7 +314,7 @@ def generate_latency_plot(output_path: Path) -> None:
 
 def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     """Log relevance-budgeted analysis tables."""
-    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{int(RELEVANCE_FLOOR*100)}% baseline nDCG) ===")
+    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{int(RELEVANCE_FLOOR * 100)}% baseline nDCG) ===")
 
     # Per-dataset recommendations
     recommendations = _compute_relevance_budgeted_scores(all_data)
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index 8595560..450aa36 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -1,5 +1,3 @@
-"""Core benchmark execution logic."""
-
 from __future__ import annotations
 
 import json
@@ -20,18 +18,7 @@
 
 
 def run_benchmark(config: BenchmarkConfig) -> dict:
-    """
-    Run the benchmark suite.
-
-    Args:
-    ----
-        config: Benchmark configuration
-
-    Returns:
-    -------
-        Results dictionary with config and per-strategy metrics
-
-    """
+    """Run benchmark suite and return results dictionary."""
     if config.dataset is None:
         raise ValueError("config.dataset must be specified")
 

From 5dc3de77550d41f527a0b571ba2caf43f34b9199 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 16:54:48 +0100
Subject: [PATCH 33/69] Made code more consistent

---
 benchmarks/__main__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index f102db9..946d7f4 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -5,7 +5,7 @@
 import zipfile
 from pathlib import Path
 
-import requests  # type: ignore[import-untyped]
+import requests
 from tqdm import tqdm
 
 from benchmarks.core import (

From 1ecc01f2aa16ef861c78956b05a3ffd252ede108 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 23 Jan 2026 17:09:11 +0100
Subject: [PATCH 34/69] Made code more consistent

---
 benchmarks/core/config.py     | 11 ++++-------
 benchmarks/core/data.py       |  9 ++++++---
 benchmarks/core/embeddings.py | 32 +++++++++++++++-----------------
 benchmarks/core/latency.py    | 11 ++++-------
 benchmarks/core/metrics.py    | 14 ++++++++------
 benchmarks/core/runner.py     |  3 ++-
 6 files changed, 39 insertions(+), 41 deletions(-)

diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index aec0228..b8c1fe2 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from dataclasses import dataclass, field
 from pathlib import Path
 
@@ -6,12 +8,7 @@
 
 @dataclass
 class BenchmarkConfig:
-    """
-    Configuration for benchmark runs.
-
-    All parameters have sensible defaults. Only `dataset` must be specified
-    before running a benchmark.
-    """
+    """Configuration for benchmark runs."""
 
     # Dataset (name from registry or DatasetInfo)
     dataset: str | None = None
@@ -42,5 +39,5 @@ class BenchmarkConfig:
     seed: int = 42
 
     def __post_init__(self) -> None:
-        """Convert paths to Path objects."""
+        """Post-initialization processing."""
         self.output_dir = Path(self.output_dir)
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
index 7734f34..2856bf5 100644
--- a/benchmarks/core/data.py
+++ b/benchmarks/core/data.py
@@ -81,7 +81,8 @@ def load_dataset(
     # Resolve dataset info
     if isinstance(dataset, str):
         if dataset not in DATASET_REGISTRY:
-            raise ValueError(f"Unknown dataset: {dataset}. Known: {list(DATASET_REGISTRY.keys())}")
+            msg = f"Unknown dataset: {dataset}. Known: {list(DATASET_REGISTRY.keys())}"
+            raise ValueError(msg)
         info = DATASET_REGISTRY[dataset]
     else:
         info = dataset
@@ -96,7 +97,8 @@ def load_dataset(
     elif info.dataset_type == DatasetType.LASTFM:
         df = _load_lastfm(Path(info.path))
     else:
-        raise ValueError(f"Unknown dataset type: {info.dataset_type}")
+        msg = f"Unknown dataset type: {info.dataset_type}"
+        raise ValueError(msg)
 
     logger.debug(f"Loaded {len(df):,} raw interactions")
 
@@ -167,4 +169,5 @@ def _load_huggingface(dataset_id: str) -> pd.DataFrame:
         df["user"] = pd.factorize(df["user"])[0]
         return df[["user", "item", "value"]]
 
-    raise ValueError(f"Unknown HuggingFace dataset format: {list(df.columns)}")
+    msg = f"Unknown HuggingFace dataset format: {list(df.columns)}"
+    raise ValueError(msg)
diff --git a/benchmarks/core/embeddings.py b/benchmarks/core/embeddings.py
index f68a920..27419c9 100644
--- a/benchmarks/core/embeddings.py
+++ b/benchmarks/core/embeddings.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import logging
 
 import numpy as np
@@ -12,11 +14,7 @@
 
 
 def generate_embeddings(data: InteractionData, dim: int = 64, seed: int = 42) -> NDArray[np.float32]:
-    """
-    Generate item embeddings using SVD on the user-item matrix.
-
-    Returns L2-normalized embeddings of shape (n_items, dim).
-    """
+    """Generate L2-normalized item embeddings using SVD on the user-item matrix."""
     ui_matrix = sparse.coo_matrix(
         (np.ones(len(data.user_ids)), (data.user_ids, data.item_ids)),
         shape=(data.n_users, data.n_items),
@@ -38,20 +36,20 @@ def compute_similarity_matrix(embeddings: NDArray[np.float32], top_k: int = 100)
     similarity = embeddings @ embeddings.T
 
     rows, cols, data = [], [], []
-    for i in range(n_items):
-        sims = similarity[i].copy()
-        sims[i] = -np.inf  # Exclude self
+    for item_idx in range(n_items):
+        sims = similarity[item_idx].copy()
+        sims[item_idx] = -np.inf  # Exclude self
 
         if top_k < n_items - 1:
-            top_idx = np.argpartition(sims, -top_k)[-top_k:]
+            top_indices = np.argpartition(sims, -top_k)[-top_k:]
         else:
-            top_idx = np.arange(n_items)[np.arange(n_items) != i]
+            top_indices = np.arange(n_items)[np.arange(n_items) != item_idx]
 
-        for j in top_idx:
-            if sims[j] > 0:
-                rows.append(i)
-                cols.append(j)
-                data.append(sims[j])
+        for neighbor_idx in top_indices:
+            if sims[neighbor_idx] > 0:
+                rows.append(item_idx)
+                cols.append(neighbor_idx)
+                data.append(sims[neighbor_idx])
 
     return sparse.coo_matrix((data, (rows, cols)), shape=(n_items, n_items), dtype=np.float32).tocsr()
 
@@ -83,6 +81,6 @@ def get_candidates(
 
     sorted_items = sorted(scores.items(), key=lambda x: -x[1])[:max_candidates]
     return (
-        np.array([c[0] for c in sorted_items], dtype=np.int64),
-        np.array([c[1] for c in sorted_items], dtype=np.float32),
+        np.array([item[0] for item in sorted_items], dtype=np.int64),
+        np.array([item[1] for item in sorted_items], dtype=np.float32),
     )
diff --git a/benchmarks/core/latency.py b/benchmarks/core/latency.py
index cb31707..aa0f5f7 100644
--- a/benchmarks/core/latency.py
+++ b/benchmarks/core/latency.py
@@ -18,9 +18,6 @@ def run_latency_benchmark(
 
     Measures how latency scales with number of candidates for each strategy.
     Uses 256d embeddings by default (common for modern embedding models).
-
-    Note: All strategies scale linearly with k. MMR/MSD are O(k·n), while
-    DPP/SSD are O(k²·n). Additionally, SSD has O(d) factor due to Gram-Schmidt.
     """
     if candidate_sizes is None:
         candidate_sizes = [100, 250, 500, 1000, 2000, 5000]
@@ -34,9 +31,9 @@ def run_latency_benchmark(
     ]
 
     results = []
-    for n_candidates in candidate_sizes:
-        embeddings = rng.standard_normal((n_candidates, embedding_dim)).astype(np.float32)
-        scores = rng.uniform(0, 1, n_candidates).astype(np.float32)
+    for num_candidates in candidate_sizes:
+        embeddings = rng.standard_normal((num_candidates, embedding_dim)).astype(np.float32)
+        scores = rng.uniform(0, 1, num_candidates).astype(np.float32)
 
         for strategy in strategies:
             latencies = []
@@ -53,7 +50,7 @@ def run_latency_benchmark(
 
             results.append(
                 {
-                    "n_candidates": n_candidates,
+                    "n_candidates": num_candidates,
                     "strategy": strategy.value,
                     "latency_ms": float(np.median(latencies)),
                     "latency_std": float(np.std(latencies)),
diff --git a/benchmarks/core/metrics.py b/benchmarks/core/metrics.py
index 49414bd..f849f9e 100644
--- a/benchmarks/core/metrics.py
+++ b/benchmarks/core/metrics.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 from numpy.typing import NDArray
 
@@ -14,9 +16,9 @@ def mrr(recommendations: NDArray[np.int64], relevant: NDArray[np.int64]) -> floa
 def ndcg(recommendations: NDArray[np.int64], relevant: NDArray[np.int64], k: int = 10) -> float:
     """Compute Normalized Discounted Cumulative Gain at k."""
     relevant_set = set(relevant)
-    recommendations = recommendations[:k]
+    recs = recommendations[:k]
 
-    dcg = sum(1.0 / np.log2(i + 2) for i, item in enumerate(recommendations) if item in relevant_set)
+    dcg = sum(1.0 / np.log2(i + 2) for i, item in enumerate(recs) if item in relevant_set)
     idcg = sum(1.0 / np.log2(i + 2) for i in range(min(len(relevant), k)))
 
     return dcg / idcg if idcg > 0 else 0.0
@@ -29,8 +31,8 @@ def ilad(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
 
     embs = embeddings[item_ids]
     sims = embs @ embs.T
-    n = len(item_ids)
-    mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+    num_items = len(item_ids)
+    mask = np.triu(np.ones((num_items, num_items), dtype=bool), k=1)
 
     return float(np.mean(1.0 - sims[mask]))
 
@@ -42,7 +44,7 @@ def ilmd(item_ids: NDArray[np.int64], embeddings: NDArray[np.float32]) -> float:
 
     embs = embeddings[item_ids]
     sims = embs @ embs.T
-    n = len(item_ids)
-    mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+    num_items = len(item_ids)
+    mask = np.triu(np.ones((num_items, num_items), dtype=bool), k=1)
 
     return float(np.min(1.0 - sims[mask]))
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index 450aa36..fee2ba3 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -20,7 +20,8 @@
 def run_benchmark(config: BenchmarkConfig) -> dict:
     """Run benchmark suite and return results dictionary."""
     if config.dataset is None:
-        raise ValueError("config.dataset must be specified")
+        msg = "config.dataset must be specified"
+        raise ValueError(msg)
 
     rng = np.random.default_rng(config.seed)
 

From bc47591cfff7b3317e2ed7147fcb3ba56e46a8df Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sat, 24 Jan 2026 12:10:17 +0100
Subject: [PATCH 35/69] Added stars to plots

---
 benchmarks/README.md               |   4 ++--
 benchmarks/core/report.py          |  23 +++++++++++++++++++++++
 benchmarks/results/latency.png     | Bin 76956 -> 72661 bytes
 benchmarks/results/pareto_ilad.png | Bin 309414 -> 312527 bytes
 benchmarks/results/pareto_ilmd.png | Bin 319234 -> 321015 bytes
 5 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 9f44190..71019e3 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -64,13 +64,13 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 
 ![Relevance vs ILAD Tradeoff](results/pareto_ilad.png)
 
-*Higher ILAD = more overall variety in recommendations*
+*Higher ILAD = more overall variety in recommendations. ★ marks the best ILAD point across all strategies (≥95% baseline relevance).*
 
 ### ILMD (Minimum Diversity)
 
 ![Relevance vs ILMD Tradeoff](results/pareto_ilmd.png)
 
-*Higher ILMD = no very similar pairs (stricter diversity guarantee)*
+*Higher ILMD = no very similar pairs (stricter diversity guarantee). ★ marks the best ILMD point across all strategies (≥95% baseline relevance).*
 
 ## Latency
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 50c9a17..3a97aeb 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -233,6 +233,9 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
     markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
 
+    # Get best operating points for each dataset
+    budgeted_scores = _compute_relevance_budgeted_scores(all_data)
+
     for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
             strategy_points = [p for p in all_data if p["dataset"] == dataset and p["strategy"] == strategy]
@@ -252,6 +255,26 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
                     alpha=0.8,
                 )
 
+        # Add stars for best operating points (best for this specific metric)
+        if dataset in budgeted_scores:
+            # Use max_ilad for ILAD plots, max_ilmd for ILMD plots
+            goal_key = "max_ilad" if diversity_metric == "ilad" else "max_ilmd"
+            best = budgeted_scores[dataset].get(goal_key)
+            if best:
+                x_star = best["ilad"] if diversity_metric == "ilad" else best["ilmd"]
+                y_star = best["ndcg"]
+                strategy = str(best["strategy"])
+                ax.scatter(
+                    [x_star],
+                    [y_star],
+                    marker="*",
+                    s=200,
+                    c=colors[strategy],
+                    edgecolors="black",
+                    linewidths=1,
+                    zorder=10,
+                )
+
         ax.set_xlabel(f"{metric_label} →", fontsize=10)
         ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
         ax.set_title(name_map.get(dataset, dataset), fontsize=12, fontweight="bold")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index b3fd2dc3c31bf2d85353d7f79cb93581077a73fa..fd2a3608d0944e005a2baf09a8a70e405afe01ce 100644
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CrRdE7


From 07ad32c8fabfd865f5d03c2dca318c42226a50a2 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sat, 24 Jan 2026 12:23:56 +0100
Subject: [PATCH 36/69] Updated plot, updated code

---
 benchmarks/__main__.py         |   3 +--
 benchmarks/core/__init__.py    |   3 ++-
 benchmarks/core/data.py        |   3 +--
 benchmarks/core/report.py      |   5 +++--
 benchmarks/core/runner.py      |   5 ++---
 benchmarks/results/latency.png | Bin 72661 -> 80779 bytes
 6 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index 946d7f4..eef9365 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -11,6 +11,7 @@
 from benchmarks.core import (
     DATASET_REGISTRY,
     BenchmarkConfig,
+    generate_latency_plot,
     generate_report,
     run_benchmark,
 )
@@ -106,8 +107,6 @@ def cmd_report() -> None:
 
 def cmd_latency() -> None:
     """Run latency benchmark and generate plot."""
-    from benchmarks.core.report import generate_latency_plot
-
     RESULTS_DIR.mkdir(parents=True, exist_ok=True)
     generate_latency_plot(RESULTS_DIR / "latency.png")
 
diff --git a/benchmarks/core/__init__.py b/benchmarks/core/__init__.py
index 702b470..9760d54 100644
--- a/benchmarks/core/__init__.py
+++ b/benchmarks/core/__init__.py
@@ -2,7 +2,7 @@
 from benchmarks.core.data import DATASET_REGISTRY, DatasetInfo, DatasetType, InteractionData, load_dataset
 from benchmarks.core.latency import run_latency_benchmark
 from benchmarks.core.metrics import ilad, ilmd, mrr, ndcg
-from benchmarks.core.report import generate_report
+from benchmarks.core.report import generate_latency_plot, generate_report
 from benchmarks.core.runner import run_benchmark
 
 __all__ = [
@@ -11,6 +11,7 @@
     "DatasetInfo",
     "DatasetType",
     "InteractionData",
+    "generate_latency_plot",
     "generate_report",
     "ilad",
     "ilmd",
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
index 2856bf5..e8bcb83 100644
--- a/benchmarks/core/data.py
+++ b/benchmarks/core/data.py
@@ -7,6 +7,7 @@
 
 import numpy as np
 import pandas as pd
+from datasets import load_dataset as hf_load_dataset
 from numpy.typing import NDArray
 
 logger = logging.getLogger(__name__)
@@ -148,8 +149,6 @@ def _load_lastfm(path: Path) -> pd.DataFrame:
 
 def _load_huggingface(dataset_id: str) -> pd.DataFrame:
     """Load a dataset from HuggingFace Hub."""
-    from datasets import load_dataset as hf_load_dataset
-
     logger.debug(f"Loading from HuggingFace: {dataset_id}")
     ds = hf_load_dataset(dataset_id, split="train")
     df = ds.to_pandas()
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 3a97aeb..ac3dddb 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -7,6 +7,8 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+from benchmarks.core.latency import run_latency_benchmark
+
 logger = logging.getLogger(__name__)
 
 # Set up seaborn styling
@@ -296,8 +298,6 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
 def generate_latency_plot(output_path: Path) -> None:
     """Generate latency scaling plot using synthetic benchmark."""
-    from benchmarks.core.latency import run_latency_benchmark
-
     logger.info("Running synthetic latency benchmark...")
     results = run_latency_benchmark()
 
@@ -322,6 +322,7 @@ def generate_latency_plot(output_path: Path) -> None:
             label=strategy.upper(),
             markersize=8,
             linewidth=2.5,
+            alpha=0.8,
         )
 
     ax.set_xlabel("Number of Candidates (n)", fontsize=11)
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index fee2ba3..1290409 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -2,6 +2,7 @@
 
 import json
 import logging
+from collections import defaultdict
 from datetime import datetime, timezone
 
 import numpy as np
@@ -140,9 +141,7 @@ def _evaluate_user(
 
 def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dict]:
     """Aggregate per-user results by strategy/diversity into mean and std."""
-    from collections import defaultdict
-
-    groups = defaultdict(list)
+    groups: dict[tuple[str, float], list[dict]] = defaultdict(list)
     for r in results:
         key = (r["strategy"], r["diversity"])
         groups[key].append(r)
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index fd2a3608d0944e005a2baf09a8a70e405afe01ce..fa7c3c73906225352af2029da9653fe494fc596d 100644
GIT binary patch
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From 1897f32717d1ea948b36aacc401f4379d717a318 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sat, 24 Jan 2026 20:36:57 +0100
Subject: [PATCH 37/69] Updated results, updated tables, fixed bug

---
 benchmarks/README.md                       | 101 ++--
 benchmarks/core/config.py                  |   2 +-
 benchmarks/core/report.py                  | 152 +++---
 benchmarks/results/amazon-video-games.json | 532 ++++++++++-----------
 benchmarks/results/goodreads.json          | 532 ++++++++++-----------
 benchmarks/results/lastfm.json             | 528 ++++++++++----------
 benchmarks/results/latency.png             | Bin 80779 -> 81850 bytes
 benchmarks/results/ml-32m.json             | 532 ++++++++++-----------
 benchmarks/results/pareto_ilad.png         | Bin 312527 -> 331384 bytes
 benchmarks/results/pareto_ilmd.png         | Bin 321015 -> 344121 bytes
 10 files changed, 1215 insertions(+), 1164 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 71019e3..0f8c53d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -26,28 +26,43 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains **≥95% of baseline relevance** (see [Methodology](#methodology) for details).
+We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains a minimum percentage of baseline relevance (see [Methodology](#methodology) for details).
 
 ### Overall Results
 
-| Strategy | Combined Score | ILAD | ILMD |
-|----------|:--------------:|:----:|:----:|
-| **DPP**  | **0.439**      | 0.75 | **0.26** |
-| SSD      | 0.236          | 0.62 | 0.17 |
-| MSD      | 0.226          | **0.78** | 0.15 |
-| MMR      | 0.213          | 0.58 | 0.18 |
+We report results at two relevance floors:
+- **Strict (99%)**: Near-zero relevance loss, production-safe
+- **Default (95%)**: Balanced tradeoff with more diversity headroom
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains relative to baseline (higher = better). ILAD/ILMD = values at best operating point, averaged across datasets while maintaining ≥95% baseline relevance.*
+#### Strict Floor (99% Relevance)
 
-**DPP wins overall** by balancing both ILAD (variety) and ILMD (no duplicates). MSD achieves highest ILAD but at the cost of ILMD.
+| Strategy | Combined | ILAD | ILMD | Best For |
+|----------|:--------:|:----:|:----:|----------|
+| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | Overall balance, avoiding similar pairs |
+| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | Simple baseline |
+| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | Sequence-aware feeds |
+| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | Maximum variety (ILAD) |
+
+#### Default Floor (95% Relevance)
+
+| Strategy | Combined | ILAD | ILMD | Best For |
+|----------|:--------:|:----:|:----:|----------|
+| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | Overall balance, avoiding similar pairs |
+| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | Sequence-aware feeds |
+| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | Simple baseline |
+| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | Maximum variety (ILAD) |
+
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement vs baseline (diversity=0).*
+
+**DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement—a substantial diversity boost for just 5% relevance cost.
 
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
 | **Best overall balance** | **DPP** | 0.8-0.9 | Wins both overall and ILMD |
-| **Maximum diversity** | **MSD** | 0.5-0.6 | Best ILAD while keeping relevance |
-| **Avoid similar pairs** | **DPP** | 0.8-0.9 | Best at preventing near-duplicates |
+| **Maximum diversity** | **MSD** | 0.4-0.5 (99%) / 0.5-0.6 (95%) | Best ILAD while keeping relevance |
+| **Avoid similar pairs** | **DPP** | 0.7-0.9 | Best worst-case diversity (highest ILMD) |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
 | **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
 
@@ -70,7 +85,7 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 
 ![Relevance vs ILMD Tradeoff](results/pareto_ilmd.png)
 
-*Higher ILMD = no very similar pairs (stricter diversity guarantee). ★ marks the best ILMD point across all strategies (≥95% baseline relevance).*
+*Higher ILMD = better worst-case diversity (fewer similar pairs). ★ marks the best ILMD point across all strategies (≥95% baseline relevance).*
 
 ## Latency
 
@@ -109,19 +124,31 @@ python -m benchmarks report
 ## Detailed Results
 
 <details>
-<summary>Per-Dataset Best Configs (≥95% baseline nDCG)</summary>
+<summary>Per-Dataset Best Configs (≥99% baseline nDCG - Strict)</summary>
+
+| Dataset | Max ILAD | Max ILMD | Best Overall |
+|---------|:--------:|:--------:|:------------:|
+| MovieLens-32M | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
+| Last.FM | DPP (`diversity`=0.7) | DPP (`diversity`=0.7) | DPP (`diversity`=0.7) |
+| Amazon-VG | MSD (`diversity`=0.4) | MMR (`diversity`=0.7) | MMR (`diversity`=0.7) |
+| Goodreads | MSD (`diversity`=0.4) | DPP (`diversity`=0.7) | DPP (`diversity`=0.7) |
+
+</details>
+
+<details>
+<summary>Per-Dataset Best Configs (≥95% baseline nDCG - Default)</summary>
 
 | Dataset | Max ILAD | Max ILMD | Best Overall |
 |---------|:--------:|:--------:|:------------:|
 | MovieLens-32M | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
 | Last.FM | MSD (`diversity`=0.6) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
-| Amazon-VG | MSD (`diversity`=0.5) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
+| Amazon-VG | MSD (`diversity`=0.5) | MMR (`diversity`=0.7) | MSD (`diversity`=0.5) |
 | Goodreads | MSD (`diversity`=0.5) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 
 </details>
 
 <details>
-<summary>Per-Dataset Detailed Metrics</summary>
+<summary>Per-Dataset Detailed Metrics (95% floor)</summary>
 
 The tables below show best achievable metrics per strategy while maintaining ≥95% of baseline nDCG.
 
@@ -129,37 +156,37 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 
 | Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
 |----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.9              | 0.052 | 0.67 | 0.25 | **0.46** |
-| SSD      | 0.8              | 0.054 | 0.58 | 0.18 | 0.32     |
-| MSD      | 0.5              | 0.055 | 0.64 | 0.10 | 0.25     |
-| MMR      | 0.7              | 0.056 | 0.54 | 0.14 | 0.27     |
+| **DPP**  | 0.9              | 0.052 | 0.67 | 0.31 | **0.46** |
+| SSD      | 0.9              | 0.053 | 0.60 | 0.22 | 0.32     |
+| MSD      | 0.6              | 0.055 | 0.67 | 0.13 | 0.25     |
+| MMR      | 0.8              | 0.055 | 0.56 | 0.17 | 0.27     |
 
 #### Last.FM
 
 | Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
 |----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.8              | 0.151 | 0.73 | 0.19 | **0.37** |
-| SSD      | 0.9              | 0.152 | 0.55 | 0.12 | 0.18     |
-| MSD      | 0.6              | 0.154 | 0.84 | 0.10 | 0.22     |
-| MMR      | 0.8              | 0.155 | 0.57 | 0.15 | 0.19     |
+| **DPP**  | 0.8              | 0.151 | 0.61 | 0.22 | **0.37** |
+| SSD      | 0.9              | 0.152 | 0.52 | 0.14 | 0.18     |
+| MSD      | 0.6              | 0.154 | 0.63 | 0.13 | 0.22     |
+| MMR      | 0.8              | 0.153 | 0.52 | 0.16 | 0.19     |
 
 #### Amazon Video Games
 
 | Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
 |----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.9              | 0.306 | 0.92 | 0.47 | **0.68** |
-| SSD      | 0.8              | 0.298 | 0.85 | 0.31 | 0.38     |
-| MSD      | 0.5              | 0.269 | 0.96 | 0.37 | 0.32     |
-| MMR      | 0.8              | 0.285 | 0.72 | 0.31 | 0.27     |
+| **MSD**  | 0.5              | 0.269 | 0.95 | 0.48 | **0.50** |
+| DPP      | 0.9              | 0.279 | 0.89 | 0.52 | 0.48     |
+| SSD      | 0.9              | 0.271 | 0.81 | 0.35 | 0.38     |
+| MMR      | 0.7              | 0.289 | 0.88 | 0.53 | 0.27     |
 
 #### Goodreads
 
 | Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
 |----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.8              | 0.027 | 0.67 | 0.14 | **0.24** |
-| SSD      | 0.9              | 0.026 | 0.50 | 0.08 | 0.10     |
-| MSD      | 0.5              | 0.027 | 0.78 | 0.08 | 0.13     |
-| MMR      | 0.7              | 0.028 | 0.50 | 0.10 | 0.12     |
+| **DPP**  | 0.8              | 0.027 | 0.60 | 0.17 | **0.24** |
+| SSD      | 0.9              | 0.026 | 0.47 | 0.10 | 0.10     |
+| MSD      | 0.5              | 0.027 | 0.67 | 0.11 | 0.13     |
+| MMR      | 0.7              | 0.028 | 0.44 | 0.11 | 0.12     |
 
 </details>
 
@@ -179,7 +206,7 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
-- **Selection**: k=20 items selected by each strategy
+- **Selection**: k=10 items selected by each strategy
 - **diversity sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
 ### Relevance-Budgeted Evaluation
@@ -187,12 +214,16 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 We use a **relevance floor** approach to ensure fair comparison:
 
 1. **Baseline**: For each dataset, compute baseline nDCG at `diversity=0` (no diversification)
-2. **Filter**: Keep only configs where nDCG ≥ 95% of baseline to ensure relevance
+2. **Filter**: Keep only configs where nDCG meets the relevance floor
 3. **Compare**: Among feasible configs, find which strategy achieves:
    - **Max ILAD**: Best overall diversity spread
-   - **Max ILMD**: Best worst-case diversity (no similar pairs)
+   - **Max ILMD**: Best worst-case diversity (fewer similar pairs)
    - **Best Combined**: Geometric mean of normalized ILAD and ILMD gains
 
+We report results at two relevance floors:
+- **Strict (99%)**: Near-zero relevance loss, production-safe
+- **Default (95%)**: Balanced tradeoff with more diversity headroom
+
 The **Combined Score** normalizes gains relative to baseline (λ=0):
 - `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`
 - `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_max - ILMD_baseline)`
@@ -205,7 +236,7 @@ This ensures a strategy must improve *both* metrics to score well—high only if
 | Metric | Type | Description |
 |--------|------|-------------|
 | **ILAD** | Average | Mean pairwise distance—measures overall variety |
-| **ILMD** | Minimum | Min pairwise distance—best at preventing near-duplicates |
+| **ILMD** | Minimum | Min pairwise distance—higher = fewer similar pairs |
 
 Both are computed as `1 - cosine_similarity` between item embeddings.
 
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index b8c1fe2..75b44d6 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -23,7 +23,7 @@ class BenchmarkConfig:
     max_candidates: int = 1000
 
     # Evaluation
-    k: int = 20
+    k: int = 10
     sample_users: int = 2000
 
     # Strategies
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index ac3dddb..314f778 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -17,18 +17,19 @@
 
 STRATEGIES = ["mmr", "msd", "dpp", "ssd"]
 
-# Relevance floor: keep configs with at least this fraction of baseline nDCG
-RELEVANCE_FLOOR = 0.95
+# Relevance floors: keep configs with at least this fraction of baseline nDCG
+RELEVANCE_FLOORS = [0.99, 0.95]  # Strict and default
 
 
 def _compute_relevance_budgeted_scores(
     all_data: list[dict],
+    relevance_floor: float = 0.95,
 ) -> dict[str, dict[str, dict[str, float | str]]]:
     """
     Compute best configs per strategy under a relevance floor constraint.
 
     For each dataset, finds strategies that maximize ILAD, ILMD, and combined diversity
-    while maintaining >= 95% of baseline (lambda=0) nDCG.
+    while maintaining >= relevance_floor of baseline (lambda=0) nDCG.
 
     Returns dict mapping dataset -> goal -> {strategy, lambda, ndcg, ilad, ilmd, score}
     """
@@ -49,7 +50,7 @@ def _compute_relevance_budgeted_scores(
         if baseline_ndcg == 0:
             continue
 
-        ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
+        ndcg_floor = relevance_floor * baseline_ndcg
 
         # Find baseline (lambda=0) and max for normalization
         ilad_baseline = (
@@ -101,8 +102,9 @@ def combined_score(point: dict) -> float:
             ilmd_range = ilmd_max - ilmd_baseline
             if ilad_range == 0 or ilmd_range == 0:
                 return 0.0
-            ilad_gain = (point["ilad"] - ilad_baseline) / ilad_range
-            ilmd_gain = (point["ilmd"] - ilmd_baseline) / ilmd_range
+            # Clamp gains to [0, 1] to handle noise/non-monotonic behavior
+            ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+            ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
             return (ilad_gain * ilmd_gain) ** 0.5  # Geometric mean
 
         # Tie-break by higher nDCG, lower lambda
@@ -152,16 +154,19 @@ def _get_dataset_baseline_and_bounds(
     return baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max
 
 
-def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, float]]:
+def _compute_strategy_scorecard(all_data: list[dict], relevance_floor: float = 0.95) -> dict[str, dict[str, float]]:
     """
     Compute per-strategy aggregate scores across all datasets.
 
-    Returns combined score, ILAD/ILMD at best operating point, nDCG retention, and typical diversity.
+    Returns combined score, ILAD/ILMD at best operating point, percentage gains vs baseline,
+    nDCG retention, and typical diversity.
     """
     datasets = sorted(set(p["dataset"] for p in all_data))
     strategy_scores: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_ilads: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_ilmds: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ilad_pct_gains: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+    strategy_ilmd_pct_gains: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_ndcg_ret: dict[str, list[float]] = {s: [] for s in STRATEGIES}
     strategy_lambdas: dict[str, list[float]] = {s: [] for s in STRATEGIES}
 
@@ -172,13 +177,14 @@ def _compute_strategy_scorecard(all_data: list[dict]) -> dict[str, dict[str, flo
             continue
 
         baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max = bounds
-        ndcg_floor = RELEVANCE_FLOOR * baseline_ndcg
+        ndcg_floor = relevance_floor * baseline_ndcg
         ilad_range = ilad_max - ilad_baseline
         ilmd_range = ilmd_max - ilmd_baseline
 
         def combined_score(point: dict) -> float:
-            ilad_gain = (point["ilad"] - ilad_baseline) / ilad_range
-            ilmd_gain = (point["ilmd"] - ilmd_baseline) / ilmd_range
+            # Clamp gains to [0, 1] to handle noise/non-monotonic behavior
+            ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+            ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
             return (ilad_gain * ilmd_gain) ** 0.5
 
         for strategy in STRATEGIES:
@@ -195,6 +201,14 @@ def combined_score(point: dict) -> float:
             strategy_ndcg_ret[strategy].append(best_point["ndcg"] / baseline_ndcg)
             strategy_lambdas[strategy].append(best_point["lambda"])
 
+            # Compute percentage gain over baseline
+            if ilad_baseline > 0:
+                ilad_pct_gain = (best_point["ilad"] - ilad_baseline) / ilad_baseline * 100
+                strategy_ilad_pct_gains[strategy].append(ilad_pct_gain)
+            if ilmd_baseline > 0:
+                ilmd_pct_gain = (best_point["ilmd"] - ilmd_baseline) / ilmd_baseline * 100
+                strategy_ilmd_pct_gains[strategy].append(ilmd_pct_gain)
+
     # Aggregate across datasets
     scorecard: dict[str, dict[str, float]] = {}
     for strategy in STRATEGIES:
@@ -202,12 +216,16 @@ def combined_score(point: dict) -> float:
             scores = strategy_scores[strategy]
             ilads = strategy_ilads[strategy]
             ilmds = strategy_ilmds[strategy]
+            ilad_pct = strategy_ilad_pct_gains[strategy]
+            ilmd_pct = strategy_ilmd_pct_gains[strategy]
             ndcg_rets = strategy_ndcg_ret[strategy]
             lambdas = strategy_lambdas[strategy]
             scorecard[strategy] = {
                 "combined_score": sum(scores) / len(scores),
                 "ilad_at_best": sum(ilads) / len(ilads),
                 "ilmd_at_best": sum(ilmds) / len(ilmds),
+                "ilad_pct_gain": sum(ilad_pct) / len(ilad_pct) if ilad_pct else 0.0,
+                "ilmd_pct_gain": sum(ilmd_pct) / len(ilmd_pct) if ilmd_pct else 0.0,
                 "ndcg_retention": sum(ndcg_rets) / len(ndcg_rets),
                 "typical_diversity": sum(lambdas) / len(lambdas),
             }
@@ -235,8 +253,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
 
     markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
 
-    # Get best operating points for each dataset
-    budgeted_scores = _compute_relevance_budgeted_scores(all_data)
+    # Get best operating points for each dataset (use default 95% floor)
+    budgeted_scores = _compute_relevance_budgeted_scores(all_data, relevance_floor=0.95)
 
     for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
@@ -338,65 +356,67 @@ def generate_latency_plot(output_path: Path) -> None:
 
 def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     """Log relevance-budgeted analysis tables."""
-    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{int(RELEVANCE_FLOOR * 100)}% baseline nDCG) ===")
-
-    # Per-dataset recommendations
-    recommendations = _compute_relevance_budgeted_scores(all_data)
+    for floor in RELEVANCE_FLOORS:
+        floor_pct = int(floor * 100)
+        logger.info(f"\n=== Relevance-Budgeted Analysis (≥{floor_pct}% baseline nDCG) ===")
+
+        # Per-dataset recommendations
+        recommendations = _compute_relevance_budgeted_scores(all_data, relevance_floor=floor)
+
+        name_map = {
+            "ml-32m": "MovieLens-32M",
+            "lastfm": "Last.FM",
+            "amazon-video-games": "Amazon Video Games",
+            "goodreads": "Goodreads",
+        }
 
-    name_map = {
-        "ml-32m": "MovieLens-32M",
-        "lastfm": "Last.FM",
-        "amazon-video-games": "Amazon Video Games",
-        "goodreads": "Goodreads",
-    }
+        logger.info(f"\nBest configs per dataset (maintaining ≥{floor_pct}% baseline relevance):")
+        logger.info("| Dataset | Goal | Strategy | λ | nDCG vs Base | ILAD | ILMD |")
+        logger.info("|---------|------|----------|---|--------------|------|------|")
+
+        for dataset, goals in recommendations.items():
+            display_name = name_map.get(dataset, dataset)[:16]
+            for goal, config in goals.items():
+                goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+                strategy_name = str(config["strategy"]).upper()
+                logger.info(
+                    f"| {display_name:16} | {goal_display:12} | {strategy_name:8} | "
+                    f"{config['lambda']:.1f} | {config['ndcg_vs_baseline']:.1%} | "
+                    f"{config['ilad']:.3f} | {config['ilmd']:.3f} |"
+                )
 
-    logger.info("\nBest configs per dataset (maintaining ≥95% baseline relevance):")
-    logger.info("| Dataset | Goal | Strategy | λ | nDCG vs Base | ILAD | ILMD |")
-    logger.info("|---------|------|----------|---|--------------|------|------|")
+        # Strategy scorecard
+        scorecard = _compute_strategy_scorecard(all_data, relevance_floor=floor)
+        logger.info(f"\n=== Strategy Scorecard at {floor_pct}% floor (averaged across datasets) ===")
+        logger.info("| Strategy | Combined | ILAD | ILMD | nDCG Ret | Typical diversity |")
+        logger.info("|----------|----------|------|------|----------|-------------------|")
 
-    for dataset, goals in recommendations.items():
-        display_name = name_map.get(dataset, dataset)[:16]
-        for goal, config in goals.items():
-            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
-            strategy_name = str(config["strategy"]).upper()
+        sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
+        for strategy, scores in sorted_strategies:
             logger.info(
-                f"| {display_name:16} | {goal_display:12} | {strategy_name:8} | "
-                f"{config['lambda']:.1f} | {config['ndcg_vs_baseline']:.1%} | "
-                f"{config['ilad']:.3f} | {config['ilmd']:.3f} |"
+                f"| {strategy.upper():8} | {scores['combined_score']:.3f}    | "
+                f"{scores['ilad_at_best']:.2f} | {scores['ilmd_at_best']:.2f} | "
+                f"{scores['ndcg_retention']:.1%}    | {scores['typical_diversity']:.1f}               |"
             )
 
-    # Strategy scorecard
-    scorecard = _compute_strategy_scorecard(all_data)
-    logger.info("\n=== Strategy Scorecard (averaged across datasets) ===")
-    logger.info("| Strategy | Combined | ILAD | ILMD | nDCG Ret | Typical diversity |")
-    logger.info("|----------|----------|------|------|----------|-------------------|")
-
-    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
-    for strategy, scores in sorted_strategies:
-        logger.info(
-            f"| {strategy.upper():8} | {scores['combined_score']:.3f}    | "
-            f"{scores['ilad_at_best']:.2f} | {scores['ilmd_at_best']:.2f} | "
-            f"{scores['ndcg_retention']:.1%}    | {scores['typical_diversity']:.1f}               |"
-        )
-
-    # Summary
-    logger.info("\n=== Key Findings ===")
-    if sorted_strategies:
-        best_strategy = sorted_strategies[0][0].upper()
-        logger.info(f"Best overall (balanced ILAD+ILMD): {best_strategy}")
-
-    # Count wins per goal
-    goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
-    for goals in recommendations.values():
-        for goal, config in goals.items():
-            strategy_str = str(config["strategy"])
-            goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
-
-    for goal, wins in goal_wins.items():
-        if wins:
-            winner = max(wins.items(), key=lambda x: x[1])
-            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
-            logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
+        # Summary
+        logger.info(f"\n=== Key Findings ({floor_pct}% floor) ===")
+        if sorted_strategies:
+            best_strategy = sorted_strategies[0][0].upper()
+            logger.info(f"Best overall (balanced ILAD+ILMD): {best_strategy}")
+
+        # Count wins per goal
+        goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
+        for goals in recommendations.values():
+            for goal, config in goals.items():
+                strategy_str = str(config["strategy"])
+                goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
+
+        for goal, wins in goal_wins.items():
+            if wins:
+                winner = max(wins.items(), key=lambda x: x[1])
+                goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+                logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
 
 
 def generate_report(results_dir: Path) -> None:
diff --git a/benchmarks/results/amazon-video-games.json b/benchmarks/results/amazon-video-games.json
index 1c1c46d..3cb5acb 100644
--- a/benchmarks/results/amazon-video-games.json
+++ b/benchmarks/results/amazon-video-games.json
@@ -1,12 +1,12 @@
 {
   "dataset": "amazon-video-games",
-  "timestamp": "2026-01-23T11:37:22.939387+00:00",
+  "timestamp": "2026-01-24T18:55:43.856497+00:00",
   "config": {
     "n_users": 707,
     "n_items": 537,
     "n_interactions": 5834,
     "sample_users": 707,
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 }
diff --git a/benchmarks/results/goodreads.json b/benchmarks/results/goodreads.json
index bc0914d..7875a90 100644
--- a/benchmarks/results/goodreads.json
+++ b/benchmarks/results/goodreads.json
@@ -1,12 +1,12 @@
 {
   "dataset": "goodreads",
-  "timestamp": "2026-01-23T10:53:49.953808+00:00",
+  "timestamp": "2026-01-24T18:56:34.142616+00:00",
   "config": {
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     "n_items": 20980,
     "n_interactions": 561514,
     "sample_users": 2000,
-    "k": 20,
+    "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
@@ -14,530 +14,530 @@
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 }
diff --git a/benchmarks/results/lastfm.json b/benchmarks/results/lastfm.json
index cd7d0a7..0407085 100644
--- a/benchmarks/results/lastfm.json
+++ b/benchmarks/results/lastfm.json
@@ -1,12 +1,12 @@
 {
   "dataset": "lastfm",
-  "timestamp": "2026-01-23T11:42:19.508300+00:00",
+  "timestamp": "2026-01-24T18:55:35.461214+00:00",
   "config": {
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     "n_items": 2823,
     "n_interactions": 71355,
     "sample_users": 1859,
-    "k": 20,
+    "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
@@ -14,386 +14,386 @@
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diff --git a/benchmarks/results/ml-32m.json b/benchmarks/results/ml-32m.json
index 586f326..01408fe 100644
--- a/benchmarks/results/ml-32m.json
+++ b/benchmarks/results/ml-32m.json
@@ -1,12 +1,12 @@
 {
   "dataset": "ml-32m",
-  "timestamp": "2026-01-23T11:41:09.158730+00:00",
+  "timestamp": "2026-01-24T18:55:04.347930+00:00",
   "config": {
     "n_users": 198973,
     "n_items": 25051,
     "n_interactions": 15880655,
     "sample_users": 2000,
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+    "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
@@ -14,530 +14,530 @@
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diff --git a/benchmarks/results/pareto_ilad.png b/benchmarks/results/pareto_ilad.png
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From 960d6d9d240d07dc3750fa95a6b2ec27623fb4a5 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sat, 24 Jan 2026 22:16:20 +0100
Subject: [PATCH 38/69] Added diversity to table

---
 benchmarks/README.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 0f8c53d..1eb4285 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -36,23 +36,23 @@ We report results at two relevance floors:
 
 #### Strict Floor (99% Relevance)
 
-| Strategy | Combined | ILAD | ILMD | Best For |
-|----------|:--------:|:----:|:----:|----------|
-| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | Overall balance, avoiding similar pairs |
-| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | Simple baseline |
-| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | Sequence-aware feeds |
-| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | Maximum variety (ILAD) |
+| Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
+|----------|:--------:|:----:|:----:|:-----------:|----------|
+| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | 0.7 | Overall balance, avoiding similar pairs |
+| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | 0.7 | Simple baseline |
+| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | 0.8 | Sequence-aware feeds |
+| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | 0.4 | Maximum variety (ILAD) |
 
 #### Default Floor (95% Relevance)
 
-| Strategy | Combined | ILAD | ILMD | Best For |
-|----------|:--------:|:----:|:----:|----------|
-| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | Overall balance, avoiding similar pairs |
-| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | Sequence-aware feeds |
-| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | Simple baseline |
-| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | Maximum variety (ILAD) |
+| Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
+|----------|:--------:|:----:|:----:|:-----------:|----------|
+| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | 0.9 | Overall balance, avoiding similar pairs |
+| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | 0.9 | Sequence-aware feeds |
+| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 | Simple baseline |
+| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 | Maximum variety (ILAD) |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement vs baseline (diversity=0).*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement vs baseline (diversity=0). `diversity` shows the typical parameter value to achieve these results.*
 
 **DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement—a substantial diversity boost for just 5% relevance cost.
 

From 4b7c0d44e8528b1330e6022b6409925540832160 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 07:29:07 +0100
Subject: [PATCH 39/69] Added diversity per strategy tables

---
 benchmarks/README.md           | 161 +++++++++++++++++++++++++++++++++
 benchmarks/core/report.py      |  62 +++++++++++++
 benchmarks/results/latency.png | Bin 81850 -> 80965 bytes
 3 files changed, 223 insertions(+)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1eb4285..b1b4c97 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -190,6 +190,167 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 
 </details>
 
+<details>
+<summary>Per-Strategy Diversity Sweep (Averaged Across Datasets)</summary>
+
+Shows how each strategy's metrics change as you increase `diversity`, averaged across all 4 datasets.
+
+#### MMR
+
+| `diversity` | nDCG Retention | ILAD | ILMD |
+|:-----------:|:--------------:|:----:|:----:|
+| 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
+| 0.1         | 100.9%          | 0.42 (+1%) | 0.13 (+2%) |
+| 0.2         | 101.7%          | 0.43 (+3%) | 0.13 (+5%) |
+| 0.3         | 102.9%          | 0.44 (+4%) | 0.14 (+9%) |
+| 0.4         | 103.4%          | 0.45 (+7%) | 0.15 (+15%) |
+| 0.5         | 103.0%          | 0.46 (+10%) | 0.16 (+23%) |
+| 0.6         | 103.5%          | 0.48 (+16%) | 0.18 (+36%) |
+| 0.7         | 103.1%          | 0.52 (+24%) | 0.22 (+60%) |
+| 0.8         | 98.1%          | 0.58 (+41%) | 0.28 (+110%) |
+| 0.9         | 85.0%          | 0.71 (+79%) | 0.42 (+247%) |
+| 1.0         | 46.3%          | 0.92 (+142%) | 0.76 (+655%) |
+
+#### MSD
+
+| `diversity` | nDCG Retention | ILAD | ILMD |
+|:-----------:|:--------------:|:----:|:----:|
+| 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
+| 0.1         | 102.9%          | 0.46 (+10%) | 0.13 (+5%) |
+| 0.2         | 103.2%          | 0.50 (+22%) | 0.14 (+11%) |
+| 0.3         | 103.1%          | 0.55 (+34%) | 0.16 (+19%) |
+| 0.4         | 101.6%          | 0.60 (+48%) | 0.17 (+30%) |
+| 0.5         | 98.1%          | 0.66 (+65%) | 0.20 (+45%) |
+| 0.6         | 92.0%          | 0.72 (+84%) | 0.23 (+64%) |
+| 0.7         | 82.7%          | 0.80 (+107%) | 0.26 (+93%) |
+| 0.8         | 72.6%          | 0.87 (+129%) | 0.31 (+138%) |
+| 0.9         | 56.7%          | 0.93 (+145%) | 0.40 (+224%) |
+| 1.0         | 43.4%          | 0.95 (+153%) | 0.53 (+373%) |
+
+#### DPP
+
+| `diversity` | nDCG Retention | ILAD | ILMD |
+|:-----------:|:--------------:|:----:|:----:|
+| 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
+| 0.1         | 104.8%          | 0.46 (+13%) | 0.15 (+26%) |
+| 0.2         | 105.2%          | 0.47 (+15%) | 0.15 (+29%) |
+| 0.3         | 105.2%          | 0.48 (+17%) | 0.16 (+33%) |
+| 0.4         | 105.2%          | 0.49 (+19%) | 0.16 (+38%) |
+| 0.5         | 105.1%          | 0.50 (+24%) | 0.17 (+46%) |
+| 0.6         | 105.2%          | 0.52 (+30%) | 0.19 (+57%) |
+| 0.7         | 106.9%          | 0.56 (+41%) | 0.21 (+77%) |
+| 0.8         | 103.0%          | 0.64 (+65%) | 0.25 (+118%) |
+| 0.9         | 92.4%          | 0.80 (+111%) | 0.39 (+275%) |
+| 1.0         | 44.6%          | 0.92 (+144%) | 0.74 (+623%) |
+
+#### SSD
+
+| `diversity` | nDCG Retention | ILAD | ILMD |
+|:-----------:|:--------------:|:----:|:----:|
+| 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
+| 0.1         | 100.7%          | 0.42 (+1%) | 0.12 (+1%) |
+| 0.2         | 101.5%          | 0.43 (+2%) | 0.13 (+2%) |
+| 0.3         | 102.4%          | 0.43 (+3%) | 0.13 (+4%) |
+| 0.4         | 102.7%          | 0.43 (+4%) | 0.13 (+6%) |
+| 0.5         | 104.2%          | 0.44 (+6%) | 0.14 (+10%) |
+| 0.6         | 105.6%          | 0.46 (+10%) | 0.14 (+16%) |
+| 0.7         | 105.7%          | 0.48 (+16%) | 0.15 (+25%) |
+| 0.8         | 106.5%          | 0.53 (+31%) | 0.18 (+47%) |
+| 0.9         | 92.6%          | 0.76 (+98%) | 0.29 (+146%) |
+| 1.0         | 44.0%          | 0.94 (+149%) | 0.73 (+611%) |
+
+*Percentages show gain vs baseline (diversity=0).*
+
+</details>
+
+<details>
+<summary>Full Results by Diversity Value (Per-Dataset)</summary>
+
+Complete metrics for each strategy at every `diversity` value (0.0-1.0). Use these tables to see how metrics change as you increase diversity.
+
+#### MovieLens-32M
+
+| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
+|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
+| DPP | 0.0 | 0.055 | 100% | 0.29 | +0% | 0.07 | +0% | 0.000 |
+| DPP | 0.1 | 0.061 | 112% | 0.34 | +18% | 0.09 | +36% | 0.067 |
+| DPP | 0.2 | 0.061 | 112% | 0.35 | +20% | 0.09 | +40% | 0.074 |
+| DPP | 0.3 | 0.061 | 112% | 0.36 | +23% | 0.10 | +45% | 0.084 |
+| DPP | 0.4 | 0.062 | 113% | 0.37 | +27% | 0.10 | +53% | 0.099 |
+| DPP | 0.5 | 0.063 | 115% | 0.39 | +32% | 0.11 | +62% | 0.117 |
+| DPP | 0.6 | 0.063 | 115% | 0.41 | +40% | 0.12 | +77% | 0.146 |
+| DPP | 0.7 | 0.065 | 119% | 0.45 | +55% | 0.14 | +105% | 0.200 |
+| DPP | 0.8 | 0.062 | 113% | 0.53 | +82% | 0.17 | +163% | 0.304 |
+| DPP | **0.9** | 0.052 | **95%** | 0.67 | +131% | 0.31 | +363% | **0.573** |
+| DPP | 1.0 | 0.015 | 28% | 0.80 | +174% | 0.54 | +722% | 0.932 |
+| MSD | 0.0 | 0.055 | 100% | 0.29 | +0% | 0.07 | +0% | 0.000 |
+| MSD | 0.1 | 0.056 | 102% | 0.31 | +7% | 0.07 | +2% | 0.010 |
+| MSD | 0.2 | 0.057 | 104% | 0.34 | +15% | 0.07 | +4% | 0.020 |
+| MSD | 0.3 | 0.058 | 106% | 0.37 | +26% | 0.07 | +7% | 0.036 |
+| MSD | 0.4 | 0.058 | 105% | 0.41 | +41% | 0.07 | +12% | 0.058 |
+| MSD | 0.5 | 0.055 | 101% | 0.47 | +60% | 0.08 | +18% | 0.087 |
+| MSD | **0.6** | 0.051 | **94%** | 0.54 | +84% | 0.09 | +29% | **0.129** |
+| MSD | 0.7 | 0.048 | 88% | 0.62 | +113% | 0.10 | +46% | 0.190 |
+| MSD | 0.8 | 0.041 | 75% | 0.71 | +143% | 0.11 | +73% | 0.270 |
+| MSD | 0.9 | 0.026 | 47% | 0.79 | +170% | 0.16 | +142% | 0.408 |
+| MSD | 1.0 | 0.013 | 24% | 0.83 | +185% | 0.24 | +262% | 0.579 |
+
+*Bold rows show best configs at 95% relevance floor.*
+
+#### Last.FM
+
+| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
+|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
+| DPP | 0.0 | 0.158 | 100% | 0.37 | +0% | 0.11 | +0% | 0.000 |
+| DPP | 0.1 | 0.160 | 101% | 0.41 | +12% | 0.13 | +19% | 0.043 |
+| DPP | 0.5 | 0.160 | 101% | 0.45 | +21% | 0.15 | +35% | 0.080 |
+| DPP | 0.7 | 0.158 | 100% | 0.52 | +40% | 0.18 | +64% | 0.147 |
+| DPP | **0.8** | 0.151 | **95%** | 0.61 | +67% | 0.22 | +102% | **0.241** |
+| DPP | 0.9 | 0.128 | 81% | 0.83 | +125% | 0.39 | +258% | 0.524 |
+| MSD | 0.0 | 0.158 | 100% | 0.37 | +0% | 0.11 | +0% | 0.000 |
+| MSD | 0.3 | 0.161 | 102% | 0.44 | +20% | 0.11 | +4% | 0.028 |
+| MSD | **0.6** | 0.154 | **97%** | 0.63 | +72% | 0.13 | +22% | **0.116** |
+| MSD | 0.7 | 0.143 | 90% | 0.77 | +109% | 0.16 | +44% | 0.201 |
+| MSD | 0.9 | 0.102 | 65% | 0.97 | +165% | 0.36 | +227% | 0.563 |
+
+*Bold rows show best configs at 95% relevance floor.*
+
+#### Amazon Video Games
+
+| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
+|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
+| DPP | 0.0 | 0.279 | 100% | 0.71 | +0% | 0.25 | +0% | 0.000 |
+| DPP | 0.5 | 0.292 | 105% | 0.76 | +7% | 0.33 | +32% | 0.142 |
+| DPP | 0.7 | 0.297 | 106% | 0.79 | +11% | 0.37 | +51% | 0.219 |
+| DPP | **0.9** | 0.306 | **110%** | 0.88 | +23% | 0.52 | +112% | **0.476** |
+| DPP | 1.0 | 0.171 | 61% | 0.99 | +39% | 0.89 | +261% | 0.944 |
+| MSD | 0.0 | 0.279 | 100% | 0.71 | +0% | 0.25 | +0% | 0.000 |
+| MSD | 0.3 | 0.288 | 103% | 0.86 | +21% | 0.35 | +42% | 0.278 |
+| MSD | **0.5** | 0.269 | **96%** | 0.95 | +34% | 0.48 | +96% | **0.529** |
+| MSD | 0.7 | 0.214 | 77% | 1.00 | +40% | 0.64 | +160% | 0.749 |
+| MSD | 0.9 | 0.170 | 61% | 1.02 | +43% | 0.76 | +209% | 0.883 |
+
+*Bold rows show best configs at 95% relevance floor.*
+
+#### Goodreads
+
+| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
+|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
+| DPP | 0.0 | 0.028 | 100% | 0.31 | +0% | 0.07 | +0% | 0.000 |
+| DPP | 0.5 | 0.028 | 100% | 0.41 | +34% | 0.11 | +53% | 0.098 |
+| DPP | 0.7 | 0.029 | 103% | 0.49 | +59% | 0.14 | +87% | 0.166 |
+| DPP | **0.8** | 0.027 | **97%** | 0.60 | +97% | 0.17 | +136% | **0.264** |
+| DPP | 0.9 | 0.024 | 84% | 0.81 | +164% | 0.34 | +365% | 0.565 |
+| MSD | 0.0 | 0.028 | 100% | 0.31 | +0% | 0.07 | +0% | 0.000 |
+| MSD | 0.3 | 0.028 | 101% | 0.52 | +69% | 0.09 | +21% | 0.088 |
+| MSD | **0.5** | 0.027 | **96%** | 0.67 | +117% | 0.11 | +53% | **0.182** |
+| MSD | 0.7 | 0.021 | 76% | 0.81 | +165% | 0.16 | +121% | 0.326 |
+| MSD | 0.9 | 0.015 | 54% | 0.93 | +203% | 0.31 | +320% | 0.588 |
+
+*Bold rows show best configs at 95% relevance floor.*
+
+</details>
+
 ## Datasets
 
 | Dataset | Domain | Interactions | Source |
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 314f778..be60088 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -354,6 +354,65 @@ def generate_latency_plot(output_path: Path) -> None:
     plt.close()
 
 
+def _log_per_strategy_diversity_tables(all_data: list[dict]) -> None:
+    """Log per-strategy tables showing metrics across diversity values (averaged over datasets)."""
+    datasets = sorted(set(p["dataset"] for p in all_data))
+    lambda_values = sorted(set(p["lambda"] for p in all_data))
+
+    logger.info("\n=== Per-Strategy Diversity Sweep (Averaged Across Datasets) ===")
+
+    for strategy in STRATEGIES:
+        logger.info(f"\n#### {strategy.upper()}")
+        logger.info("")
+        logger.info("| `diversity` | nDCG Retention | ILAD | ILMD |")
+        logger.info("|:-----------:|:--------------:|:----:|:----:|")
+
+        for lam in lambda_values:
+            ndcg_rets = []
+            ilads = []
+            ilmds = []
+            ilad_pct_gains = []
+            ilmd_pct_gains = []
+
+            for dataset in datasets:
+                dataset_points = [p for p in all_data if p["dataset"] == dataset]
+                bounds = _get_dataset_baseline_and_bounds(dataset_points)
+                if bounds is None:
+                    continue
+
+                baseline_ndcg, ilad_baseline, _, ilmd_baseline, _ = bounds
+
+                # Find point for this strategy/lambda
+                point = next(
+                    (p for p in dataset_points if p["strategy"] == strategy and p["lambda"] == lam),
+                    None,
+                )
+                if point is None:
+                    continue
+
+                ndcg_rets.append(point["ndcg"] / baseline_ndcg if baseline_ndcg > 0 else 0)
+                ilads.append(point["ilad"])
+                ilmds.append(point["ilmd"])
+
+                if ilad_baseline > 0:
+                    ilad_pct_gains.append((point["ilad"] - ilad_baseline) / ilad_baseline * 100)
+                if ilmd_baseline > 0:
+                    ilmd_pct_gains.append((point["ilmd"] - ilmd_baseline) / ilmd_baseline * 100)
+
+            if ndcg_rets:
+                avg_ndcg_ret = sum(ndcg_rets) / len(ndcg_rets)
+                avg_ilad = sum(ilads) / len(ilads)
+                avg_ilmd = sum(ilmds) / len(ilmds)
+                avg_ilad_gain = sum(ilad_pct_gains) / len(ilad_pct_gains) if ilad_pct_gains else 0
+                avg_ilmd_gain = sum(ilmd_pct_gains) / len(ilmd_pct_gains) if ilmd_pct_gains else 0
+
+                logger.info(
+                    f"| {lam:.1f}         | {avg_ndcg_ret:.1%}          | "
+                    f"{avg_ilad:.2f} (+{avg_ilad_gain:.0f}%) | "
+                    f"{avg_ilmd:.2f} (+{avg_ilmd_gain:.0f}%) |"
+                )
+
+
 def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
     """Log relevance-budgeted analysis tables."""
     for floor in RELEVANCE_FLOORS:
@@ -463,4 +522,7 @@ def generate_report(results_dir: Path) -> None:
     # Relevance-budgeted analysis
     _log_relevance_budgeted_analysis(all_data)
 
+    # Per-strategy diversity sweep tables
+    _log_per_strategy_diversity_tables(all_data)
+
     logger.info(f"\nReport generated: {results_dir}")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From c256b87711c7e065628b5e0117e2fe089b08aa70 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 07:34:17 +0100
Subject: [PATCH 40/69] Updated results

---
 benchmarks/README.md | 107 ++++++-------------------------------------
 1 file changed, 13 insertions(+), 94 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index b1b4c97..4ef597a 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -31,10 +31,10 @@ We use a **relevance-budgeted** approach: for each strategy, we find the best co
 ### Overall Results
 
 We report results at two relevance floors:
-- **Strict (99%)**: Near-zero relevance loss, production-safe
-- **Default (95%)**: Balanced tradeoff with more diversity headroom
+- **99%**: Near-zero relevance loss, production-safe
+- **95%**: Balanced tradeoff with more diversity headroom
 
-#### Strict Floor (99% Relevance)
+#### 99% Relevance Floor
 
 | Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
 |----------|:--------:|:----:|:----:|:-----------:|----------|
@@ -43,7 +43,7 @@ We report results at two relevance floors:
 | SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | 0.8 | Sequence-aware feeds |
 | MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | 0.4 | Maximum variety (ILAD) |
 
-#### Default Floor (95% Relevance)
+#### 95% Relevance Floor
 
 | Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
 |----------|:--------:|:----:|:----:|:-----------:|----------|
@@ -124,7 +124,7 @@ python -m benchmarks report
 ## Detailed Results
 
 <details>
-<summary>Per-Dataset Best Configs (≥99% baseline nDCG - Strict)</summary>
+<summary>Per-Dataset Best Configs (99% Relevance Floor)</summary>
 
 | Dataset | Max ILAD | Max ILMD | Best Overall |
 |---------|:--------:|:--------:|:------------:|
@@ -136,7 +136,7 @@ python -m benchmarks report
 </details>
 
 <details>
-<summary>Per-Dataset Best Configs (≥95% baseline nDCG - Default)</summary>
+<summary>Per-Dataset Best Configs (95% Relevance Floor)</summary>
 
 | Dataset | Max ILAD | Max ILMD | Best Overall |
 |---------|:--------:|:--------:|:------------:|
@@ -195,6 +195,11 @@ The tables below show best achievable metrics per strategy while maintaining ≥
 
 Shows how each strategy's metrics change as you increase `diversity`, averaged across all 4 datasets.
 
+> **Note:** nDCG Retention can exceed 100% and may not decrease monotonically. This happens because
+> diversification can sometimes *improve* relevance by breaking ties among similar items or surfacing
+> items that better match the held-out test item. This effect is dataset-dependent and typically
+> occurs at moderate diversity levels before eventually declining at high diversity.
+
 #### MMR
 
 | `diversity` | nDCG Retention | ILAD | ILMD |
@@ -263,93 +268,7 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 
 </details>
 
-<details>
-<summary>Full Results by Diversity Value (Per-Dataset)</summary>
-
-Complete metrics for each strategy at every `diversity` value (0.0-1.0). Use these tables to see how metrics change as you increase diversity.
-
-#### MovieLens-32M
-
-| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
-|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
-| DPP | 0.0 | 0.055 | 100% | 0.29 | +0% | 0.07 | +0% | 0.000 |
-| DPP | 0.1 | 0.061 | 112% | 0.34 | +18% | 0.09 | +36% | 0.067 |
-| DPP | 0.2 | 0.061 | 112% | 0.35 | +20% | 0.09 | +40% | 0.074 |
-| DPP | 0.3 | 0.061 | 112% | 0.36 | +23% | 0.10 | +45% | 0.084 |
-| DPP | 0.4 | 0.062 | 113% | 0.37 | +27% | 0.10 | +53% | 0.099 |
-| DPP | 0.5 | 0.063 | 115% | 0.39 | +32% | 0.11 | +62% | 0.117 |
-| DPP | 0.6 | 0.063 | 115% | 0.41 | +40% | 0.12 | +77% | 0.146 |
-| DPP | 0.7 | 0.065 | 119% | 0.45 | +55% | 0.14 | +105% | 0.200 |
-| DPP | 0.8 | 0.062 | 113% | 0.53 | +82% | 0.17 | +163% | 0.304 |
-| DPP | **0.9** | 0.052 | **95%** | 0.67 | +131% | 0.31 | +363% | **0.573** |
-| DPP | 1.0 | 0.015 | 28% | 0.80 | +174% | 0.54 | +722% | 0.932 |
-| MSD | 0.0 | 0.055 | 100% | 0.29 | +0% | 0.07 | +0% | 0.000 |
-| MSD | 0.1 | 0.056 | 102% | 0.31 | +7% | 0.07 | +2% | 0.010 |
-| MSD | 0.2 | 0.057 | 104% | 0.34 | +15% | 0.07 | +4% | 0.020 |
-| MSD | 0.3 | 0.058 | 106% | 0.37 | +26% | 0.07 | +7% | 0.036 |
-| MSD | 0.4 | 0.058 | 105% | 0.41 | +41% | 0.07 | +12% | 0.058 |
-| MSD | 0.5 | 0.055 | 101% | 0.47 | +60% | 0.08 | +18% | 0.087 |
-| MSD | **0.6** | 0.051 | **94%** | 0.54 | +84% | 0.09 | +29% | **0.129** |
-| MSD | 0.7 | 0.048 | 88% | 0.62 | +113% | 0.10 | +46% | 0.190 |
-| MSD | 0.8 | 0.041 | 75% | 0.71 | +143% | 0.11 | +73% | 0.270 |
-| MSD | 0.9 | 0.026 | 47% | 0.79 | +170% | 0.16 | +142% | 0.408 |
-| MSD | 1.0 | 0.013 | 24% | 0.83 | +185% | 0.24 | +262% | 0.579 |
-
-*Bold rows show best configs at 95% relevance floor.*
-
-#### Last.FM
-
-| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
-|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
-| DPP | 0.0 | 0.158 | 100% | 0.37 | +0% | 0.11 | +0% | 0.000 |
-| DPP | 0.1 | 0.160 | 101% | 0.41 | +12% | 0.13 | +19% | 0.043 |
-| DPP | 0.5 | 0.160 | 101% | 0.45 | +21% | 0.15 | +35% | 0.080 |
-| DPP | 0.7 | 0.158 | 100% | 0.52 | +40% | 0.18 | +64% | 0.147 |
-| DPP | **0.8** | 0.151 | **95%** | 0.61 | +67% | 0.22 | +102% | **0.241** |
-| DPP | 0.9 | 0.128 | 81% | 0.83 | +125% | 0.39 | +258% | 0.524 |
-| MSD | 0.0 | 0.158 | 100% | 0.37 | +0% | 0.11 | +0% | 0.000 |
-| MSD | 0.3 | 0.161 | 102% | 0.44 | +20% | 0.11 | +4% | 0.028 |
-| MSD | **0.6** | 0.154 | **97%** | 0.63 | +72% | 0.13 | +22% | **0.116** |
-| MSD | 0.7 | 0.143 | 90% | 0.77 | +109% | 0.16 | +44% | 0.201 |
-| MSD | 0.9 | 0.102 | 65% | 0.97 | +165% | 0.36 | +227% | 0.563 |
-
-*Bold rows show best configs at 95% relevance floor.*
-
-#### Amazon Video Games
-
-| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
-|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
-| DPP | 0.0 | 0.279 | 100% | 0.71 | +0% | 0.25 | +0% | 0.000 |
-| DPP | 0.5 | 0.292 | 105% | 0.76 | +7% | 0.33 | +32% | 0.142 |
-| DPP | 0.7 | 0.297 | 106% | 0.79 | +11% | 0.37 | +51% | 0.219 |
-| DPP | **0.9** | 0.306 | **110%** | 0.88 | +23% | 0.52 | +112% | **0.476** |
-| DPP | 1.0 | 0.171 | 61% | 0.99 | +39% | 0.89 | +261% | 0.944 |
-| MSD | 0.0 | 0.279 | 100% | 0.71 | +0% | 0.25 | +0% | 0.000 |
-| MSD | 0.3 | 0.288 | 103% | 0.86 | +21% | 0.35 | +42% | 0.278 |
-| MSD | **0.5** | 0.269 | **96%** | 0.95 | +34% | 0.48 | +96% | **0.529** |
-| MSD | 0.7 | 0.214 | 77% | 1.00 | +40% | 0.64 | +160% | 0.749 |
-| MSD | 0.9 | 0.170 | 61% | 1.02 | +43% | 0.76 | +209% | 0.883 |
-
-*Bold rows show best configs at 95% relevance floor.*
 
-#### Goodreads
-
-| Strategy | `diversity` | nDCG | nDCG Ret | ILAD | ILAD Gain | ILMD | ILMD Gain | Combined |
-|----------|:-----------:|:----:|:--------:|:----:|:---------:|:----:|:---------:|:--------:|
-| DPP | 0.0 | 0.028 | 100% | 0.31 | +0% | 0.07 | +0% | 0.000 |
-| DPP | 0.5 | 0.028 | 100% | 0.41 | +34% | 0.11 | +53% | 0.098 |
-| DPP | 0.7 | 0.029 | 103% | 0.49 | +59% | 0.14 | +87% | 0.166 |
-| DPP | **0.8** | 0.027 | **97%** | 0.60 | +97% | 0.17 | +136% | **0.264** |
-| DPP | 0.9 | 0.024 | 84% | 0.81 | +164% | 0.34 | +365% | 0.565 |
-| MSD | 0.0 | 0.028 | 100% | 0.31 | +0% | 0.07 | +0% | 0.000 |
-| MSD | 0.3 | 0.028 | 101% | 0.52 | +69% | 0.09 | +21% | 0.088 |
-| MSD | **0.5** | 0.027 | **96%** | 0.67 | +117% | 0.11 | +53% | **0.182** |
-| MSD | 0.7 | 0.021 | 76% | 0.81 | +165% | 0.16 | +121% | 0.326 |
-| MSD | 0.9 | 0.015 | 54% | 0.93 | +203% | 0.31 | +320% | 0.588 |
-
-*Bold rows show best configs at 95% relevance floor.*
-
-</details>
 
 ## Datasets
 
@@ -382,8 +301,8 @@ We use a **relevance floor** approach to ensure fair comparison:
    - **Best Combined**: Geometric mean of normalized ILAD and ILMD gains
 
 We report results at two relevance floors:
-- **Strict (99%)**: Near-zero relevance loss, production-safe
-- **Default (95%)**: Balanced tradeoff with more diversity headroom
+- **99%**: Near-zero relevance loss, production-safe
+- **95%**: Balanced tradeoff with more diversity headroom
 
 The **Combined Score** normalizes gains relative to baseline (λ=0):
 - `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`

From 491ee27cd0e86f51bc3bf5c37fd854ea2a2a0393 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 07:36:40 +0100
Subject: [PATCH 41/69] Updated results

---
 benchmarks/README.md | 67 ++++++++------------------------------------
 1 file changed, 12 insertions(+), 55 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 4ef597a..74d36e9 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -36,21 +36,21 @@ We report results at two relevance floors:
 
 #### 99% Relevance Floor
 
-| Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
-|----------|:--------:|:----:|:----:|:-----------:|----------|
-| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | 0.7 | Overall balance, avoiding similar pairs |
-| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | 0.7 | Simple baseline |
-| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | 0.8 | Sequence-aware feeds |
-| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | 0.4 | Maximum variety (ILAD) |
+| Strategy | Combined | ILAD | ILMD | `diversity` |
+|----------|:--------:|:----:|:----:|:-----------:|
+| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | 0.7 |
+| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | 0.7 |
+| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | 0.8 |
+| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | 0.4 |
 
 #### 95% Relevance Floor
 
-| Strategy | Combined | ILAD | ILMD | `diversity` | Best For |
-|----------|:--------:|:----:|:----:|:-----------:|----------|
-| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | 0.9 | Overall balance, avoiding similar pairs |
-| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | 0.9 | Sequence-aware feeds |
-| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 | Simple baseline |
-| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 | Maximum variety (ILAD) |
+| Strategy | Combined | ILAD | ILMD | `diversity` |
+|----------|:--------:|:----:|:----:|:-----------:|
+| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | 0.9 |
+| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | 0.9 |
+| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 |
+| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 |
 
 *Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement vs baseline (diversity=0). `diversity` shows the typical parameter value to achieve these results.*
 
@@ -147,49 +147,6 @@ python -m benchmarks report
 
 </details>
 
-<details>
-<summary>Per-Dataset Detailed Metrics (95% floor)</summary>
-
-The tables below show best achievable metrics per strategy while maintaining ≥95% of baseline nDCG.
-
-#### MovieLens-32M
-
-| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
-|----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.9              | 0.052 | 0.67 | 0.31 | **0.46** |
-| SSD      | 0.9              | 0.053 | 0.60 | 0.22 | 0.32     |
-| MSD      | 0.6              | 0.055 | 0.67 | 0.13 | 0.25     |
-| MMR      | 0.8              | 0.055 | 0.56 | 0.17 | 0.27     |
-
-#### Last.FM
-
-| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
-|----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.8              | 0.151 | 0.61 | 0.22 | **0.37** |
-| SSD      | 0.9              | 0.152 | 0.52 | 0.14 | 0.18     |
-| MSD      | 0.6              | 0.154 | 0.63 | 0.13 | 0.22     |
-| MMR      | 0.8              | 0.153 | 0.52 | 0.16 | 0.19     |
-
-#### Amazon Video Games
-
-| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
-|----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **MSD**  | 0.5              | 0.269 | 0.95 | 0.48 | **0.50** |
-| DPP      | 0.9              | 0.279 | 0.89 | 0.52 | 0.48     |
-| SSD      | 0.9              | 0.271 | 0.81 | 0.35 | 0.38     |
-| MMR      | 0.7              | 0.289 | 0.88 | 0.53 | 0.27     |
-
-#### Goodreads
-
-| Strategy | Best `diversity` | nDCG | ILAD | ILMD | Combined |
-|----------|:----------------:|:----:|:----:|:----:|:--------:|
-| **DPP**  | 0.8              | 0.027 | 0.60 | 0.17 | **0.24** |
-| SSD      | 0.9              | 0.026 | 0.47 | 0.10 | 0.10     |
-| MSD      | 0.5              | 0.027 | 0.67 | 0.11 | 0.13     |
-| MMR      | 0.7              | 0.028 | 0.44 | 0.11 | 0.12     |
-
-</details>
-
 <details>
 <summary>Per-Strategy Diversity Sweep (Averaged Across Datasets)</summary>
 

From 169e65f5d2d9c5459325f6a30a14dc3f18b8b6b7 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 07:40:20 +0100
Subject: [PATCH 42/69] Updated results

---
 benchmarks/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 74d36e9..4244cb4 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -52,7 +52,7 @@ We report results at two relevance floors:
 | MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 |
 | MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement vs baseline (diversity=0). `diversity` shows the typical parameter value to achieve these results.*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement over baseline (diversity=0). `diversity` shows the parameter value to achieve these results.*
 
 **DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement—a substantial diversity boost for just 5% relevance cost.
 
@@ -60,9 +60,9 @@ We report results at two relevance floors:
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
-| **Best overall balance** | **DPP** | 0.8-0.9 | Wins both overall and ILMD |
-| **Maximum diversity** | **MSD** | 0.4-0.5 (99%) / 0.5-0.6 (95%) | Best ILAD while keeping relevance |
-| **Avoid similar pairs** | **DPP** | 0.7-0.9 | Best worst-case diversity (highest ILMD) |
+| **Best overall balance** | **DPP** | 0.7-0.9 | Wins both overall and ILMD |
+| **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD while keeping relevance |
+| **Avoid similar pairs (ILMD)** | **DPP** | 0.7-0.9 | Best worst-case diversity |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
 | **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
 

From 1da0ebe6172e7e9340dbe1bcd96102fc9b951605 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 07:48:04 +0100
Subject: [PATCH 43/69] Added 99% threshold diamond to plot

---
 benchmarks/README.md               |   4 ++--
 benchmarks/core/report.py          |  32 +++++++++++++++++++++++------
 benchmarks/results/latency.png     | Bin 80965 -> 80972 bytes
 benchmarks/results/pareto_ilad.png | Bin 331384 -> 330635 bytes
 benchmarks/results/pareto_ilmd.png | Bin 344121 -> 343604 bytes
 5 files changed, 28 insertions(+), 8 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 4244cb4..9068a46 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -79,13 +79,13 @@ We report results at two relevance floors:
 
 ![Relevance vs ILAD Tradeoff](results/pareto_ilad.png)
 
-*Higher ILAD = more overall variety in recommendations. ★ marks the best ILAD point across all strategies (≥95% baseline relevance).*
+*Higher ILAD = more overall variety in recommendations. ★ = best at 95% floor, ◆ = best at 99% floor.*
 
 ### ILMD (Minimum Diversity)
 
 ![Relevance vs ILMD Tradeoff](results/pareto_ilmd.png)
 
-*Higher ILMD = better worst-case diversity (fewer similar pairs). ★ marks the best ILMD point across all strategies (≥95% baseline relevance).*
+*Higher ILMD = better worst-case diversity (fewer similar pairs). ★ = best at 95% floor, ◆ = best at 99% floor.*
 
 ## Latency
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index be60088..69ea0a3 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -254,7 +254,8 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
     markers = {"mmr": "o", "msd": "s", "dpp": "^", "ssd": "D"}
 
     # Get best operating points for each dataset (use default 95% floor)
-    budgeted_scores = _compute_relevance_budgeted_scores(all_data, relevance_floor=0.95)
+    budgeted_scores_95 = _compute_relevance_budgeted_scores(all_data, relevance_floor=0.95)
+    budgeted_scores_99 = _compute_relevance_budgeted_scores(all_data, relevance_floor=0.99)
 
     for ax, dataset in zip(axes, datasets):
         for strategy in strategies:
@@ -275,11 +276,12 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
                     alpha=0.8,
                 )
 
-        # Add stars for best operating points (best for this specific metric)
-        if dataset in budgeted_scores:
-            # Use max_ilad for ILAD plots, max_ilmd for ILMD plots
-            goal_key = "max_ilad" if diversity_metric == "ilad" else "max_ilmd"
-            best = budgeted_scores[dataset].get(goal_key)
+        # Add stars for best operating points at both floors
+        goal_key = "max_ilad" if diversity_metric == "ilad" else "max_ilmd"
+
+        # Filled star for 95% floor
+        if dataset in budgeted_scores_95:
+            best = budgeted_scores_95[dataset].get(goal_key)
             if best:
                 x_star = best["ilad"] if diversity_metric == "ilad" else best["ilmd"]
                 y_star = best["ndcg"]
@@ -295,6 +297,24 @@ def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metr
                     zorder=10,
                 )
 
+        # Diamond for 99% floor
+        if dataset in budgeted_scores_99:
+            best = budgeted_scores_99[dataset].get(goal_key)
+            if best:
+                x_diamond = best["ilad"] if diversity_metric == "ilad" else best["ilmd"]
+                y_diamond = best["ndcg"]
+                strategy = str(best["strategy"])
+                ax.scatter(
+                    [x_diamond],
+                    [y_diamond],
+                    marker="D",
+                    s=100,
+                    c=colors[strategy],
+                    edgecolors="black",
+                    linewidths=1,
+                    zorder=10,
+                )
+
         ax.set_xlabel(f"{metric_label} →", fontsize=10)
         ax.set_ylabel("nDCG@10 (Relevance) →", fontsize=10)
         ax.set_title(name_map.get(dataset, dataset), fontsize=12, fontweight="bold")
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index 7deadc2b2d5fc75569903b2236b507a5730ff35f..b150f99620bd7e37a686ec079ac4803c5cacac20 100644
GIT binary patch
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From 9c5b7885a8483ddaff6fd62f39b93f2b7fef56b6 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 08:23:39 +0100
Subject: [PATCH 44/69] Updated results

---
 benchmarks/README.md | 33 +++++++++++++++++++++------------
 1 file changed, 21 insertions(+), 12 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 9068a46..93efb1f 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -13,8 +13,15 @@ In addition, we measure latency of each strategy as the number of candidates sca
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard
 > collaborative filtering datasets.
 
+## TL;DR
+
+**Use DPP with `diversity=0.7-0.9`** for best overall diversity gains with minimal relevance loss.
+- At 99% relevance floor: +51% ILAD, +104% ILMD improvement
+- At 95% relevance floor: +79% ILAD, +178% ILMD improvement
+
 ## Table of Contents
 
+- [TL;DR](#tldr)
 - [Key Findings](#key-findings)
 - [Relevance-Diversity Tradeoff](#relevance-diversity-tradeoff)
 - [Latency](#latency)
@@ -26,6 +33,8 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
+We measure two diversity metrics: **ILAD** (average pairwise diversity—higher = more variety) and **ILMD** (minimum pairwise diversity—higher = fewer similar pairs).
+
 We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains a minimum percentage of baseline relevance (see [Methodology](#methodology) for details).
 
 ### Overall Results
@@ -52,9 +61,9 @@ We report results at two relevance floors:
 | MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 |
 | MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (higher = better). Percentages show improvement over baseline (diversity=0). `diversity` shows the parameter value to achieve these results.*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (0-1 scale, higher = better). A score of 0.39 means ~39% of maximum possible balanced diversity improvement. Percentages show improvement over baseline (`diversity=0`). `diversity` shows the parameter value to achieve these results.*
 
-**DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement—a substantial diversity boost for just 5% relevance cost.
+**DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement, a substantial diversity boost for at most 5% relevance cost.
 
 ### Recommendations
 
@@ -62,7 +71,7 @@ We report results at two relevance floors:
 |------|----------|:-----------:|-------|
 | **Best overall balance** | **DPP** | 0.7-0.9 | Wins both overall and ILMD |
 | **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD while keeping relevance |
-| **Avoid similar pairs (ILMD)** | **DPP** | 0.7-0.9 | Best worst-case diversity |
+| **Avoid similar pairs (ILMD)** | **DPP** | 0.7-0.9 | Best worst-case diversity (fewest near-duplicates) |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
 | **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
 
@@ -70,8 +79,8 @@ We report results at two relevance floors:
 
 > **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
 > **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
-> recently shown items. For content feeds, infinite scroll, or conversational RAG where you
-> maintain a sliding window of recent items, SSD may outperform these results.
+> recently shown items. In content feeds with sliding windows, SSD's novelty-relative-to-recent
+> approach should yield larger effective diversity gains than shown here.
 
 ## Relevance-Diversity Tradeoff
 
@@ -79,17 +88,17 @@ We report results at two relevance floors:
 
 ![Relevance vs ILAD Tradeoff](results/pareto_ilad.png)
 
-*Higher ILAD = more overall variety in recommendations. ★ = best at 95% floor, ◆ = best at 99% floor.*
+*Higher ILAD = more overall variety in recommendations. ★ = best operating point at 95% floor, ◆ = best at 99% floor.*
 
 ### ILMD (Minimum Diversity)
 
 ![Relevance vs ILMD Tradeoff](results/pareto_ilmd.png)
 
-*Higher ILMD = better worst-case diversity (fewer similar pairs). ★ = best at 95% floor, ◆ = best at 99% floor.*
+*Higher ILMD = better worst-case diversity (fewer similar pairs). ★ = best operating point at 95% floor, ◆ = best at 99% floor.*
 
 ## Latency
 
-All strategies are extremely fast in practice. The plot below shows latency scaling with the number of candidates (k=10, d=256):
+All strategies are extremely fast in practice—even with 10,000 candidates, all complete in <100ms. The plot below shows latency scaling with the number of candidates (k=10, d=256):
 
 ![Latency vs Candidates](results/latency.png)
 
@@ -126,7 +135,7 @@ python -m benchmarks report
 <details>
 <summary>Per-Dataset Best Configs (99% Relevance Floor)</summary>
 
-| Dataset | Max ILAD | Max ILMD | Best Overall |
+| Dataset | Max ILAD | Max ILMD | Best Combined |
 |---------|:--------:|:--------:|:------------:|
 | MovieLens-32M | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
 | Last.FM | DPP (`diversity`=0.7) | DPP (`diversity`=0.7) | DPP (`diversity`=0.7) |
@@ -138,7 +147,7 @@ python -m benchmarks report
 <details>
 <summary>Per-Dataset Best Configs (95% Relevance Floor)</summary>
 
-| Dataset | Max ILAD | Max ILMD | Best Overall |
+| Dataset | Max ILAD | Max ILMD | Best Combined |
 |---------|:--------:|:--------:|:------------:|
 | MovieLens-32M | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) | DPP (`diversity`=0.9) |
 | Last.FM | MSD (`diversity`=0.6) | DPP (`diversity`=0.8) | DPP (`diversity`=0.8) |
@@ -244,7 +253,7 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=10 items selected by each strategy
-- **diversity sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
+- **`diversity` sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
 ### Relevance-Budgeted Evaluation
 
@@ -261,7 +270,7 @@ We report results at two relevance floors:
 - **99%**: Near-zero relevance loss, production-safe
 - **95%**: Balanced tradeoff with more diversity headroom
 
-The **Combined Score** normalizes gains relative to baseline (λ=0):
+The **Combined Score** normalizes gains relative to baseline (`diversity=0`):
 - `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`
 - `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_max - ILMD_baseline)`
 - `Combined = sqrt(ILAD_gain × ILMD_gain)`

From cb67c9fb7a053ca13e15137768da6790fdf301bd Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 12:56:37 +0100
Subject: [PATCH 45/69] Ran 10 times for statistical significant results,
 updated results

---
 README.md                                  |   23 +
 benchmarks/README.md                       |   35 +-
 benchmarks/__main__.py                     |   38 +-
 benchmarks/core/config.py                  |    1 +
 benchmarks/core/runner.py                  |  124 +-
 benchmarks/results/amazon-video-games.json | 4252 ++++++++++++++++++--
 benchmarks/results/goodreads.json          | 4250 +++++++++++++++++--
 benchmarks/results/lastfm.json             | 4252 ++++++++++++++++++--
 benchmarks/results/latency.png             |  Bin 80972 -> 80884 bytes
 benchmarks/results/ml-32m.json             | 4250 +++++++++++++++++--
 benchmarks/results/pareto_ilad.png         |  Bin 330635 -> 316527 bytes
 benchmarks/results/pareto_ilmd.png         |  Bin 343604 -> 330944 bytes
 12 files changed, 15764 insertions(+), 1461 deletions(-)

diff --git a/README.md b/README.md
index a3438fc..5d1b0fb 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,7 @@
 
 [Quickstart](#quickstart) •
 [Supported Strategies](#supported-strategies) •
+[Benchmarks](#benchmarks) •
 [Motivation](#motivation) •
 [Examples](#examples) •
 [References](#references)
@@ -76,6 +77,28 @@ The following table describes the supported strategies, how they work, their tim
 | **COVER** (Facility-Location)         | Ensures selected items collectively represent the full dataset’s structure.                    | **O(k · n²)**             | Great for topic coverage or clustering scenarios, but slower for large `n`. |
 | **SSD** (Sliding Spectrum Decomposition) | Sequence‑aware diversification: rewards novelty relative to recently shown items.     | **O(k · n · d)**          | Great for content feeds & infinite scroll, e.g. social/news/product feeds where users consume sequentially, as well as conversational RAG to avoid showing similar chunks within the recent window.
 
+## Benchmarks
+
+We evaluated all strategies across 4 recommendation datasets (MovieLens-32M, Amazon Video Games, Goodreads, Last.FM). Full methodology and detailed results are in [`benchmarks/`](benchmarks/).
+
+**TL;DR: Use DPP with `diversity=0.7-0.9`** for best overall diversity gains with minimal relevance loss.
+
+| Relevance Floor | ILAD Improvement | ILMD Improvement |
+|-----------------|:----------------:|:----------------:|
+| 99% (strict)    | +51%             | +104%            |
+| 95% (balanced)  | +79%             | +178%            |
+
+**Quick recommendations:**
+
+| Use Case | Strategy | `diversity` |
+|----------|----------|:-----------:|
+| Best overall balance | **DPP** | 0.7–0.9 |
+| Maximum variety (ILAD) | MSD | 0.4–0.6 |
+| Fewer similar pairs (ILMD) | DPP | 0.7–0.9 |
+| Sequence-aware (feeds, RAG) | SSD | 0.7–0.9 |
+
+→ See [full benchmark results](benchmarks/) for per-dataset tables and Pareto plots.
+
 
 ## Motivation
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 93efb1f..1b039b1 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -13,15 +13,7 @@ In addition, we measure latency of each strategy as the number of candidates sca
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard
 > collaborative filtering datasets.
 
-## TL;DR
-
-**Use DPP with `diversity=0.7-0.9`** for best overall diversity gains with minimal relevance loss.
-- At 99% relevance floor: +51% ILAD, +104% ILMD improvement
-- At 95% relevance floor: +79% ILAD, +178% ILMD improvement
-
 ## Table of Contents
-
-- [TL;DR](#tldr)
 - [Key Findings](#key-findings)
 - [Relevance-Diversity Tradeoff](#relevance-diversity-tradeoff)
 - [Latency](#latency)
@@ -47,33 +39,33 @@ We report results at two relevance floors:
 
 | Strategy | Combined | ILAD | ILMD | `diversity` |
 |----------|:--------:|:----:|:----:|:-----------:|
-| **DPP**  | **0.273** | 0.59 (+51%) | **0.24 (+104%)** | 0.7 |
-| MMR      | 0.224    | 0.52 (+32%) | 0.20 (+67%)      | 0.7 |
-| SSD      | 0.195    | 0.54 (+38%) | 0.18 (+55%)      | 0.8 |
-| MSD      | 0.166    | **0.61 (+54%)** | 0.16 (+36%)  | 0.4 |
+| **DPP**  | **0.274** | 0.60 (+43%) | **0.25 (+108%)** | 0.8 |
+| SSD      | 0.195    | 0.55 (+31%) | 0.21 (+75%)      | 0.8 |
+| MMR      | 0.189    | 0.53 (+26%) | 0.22 (+83%)      | 0.7 |
+| MSD      | 0.117    | **0.57 (+36%)** | 0.16 (+33%)  | 0.3 |
 
 #### 95% Relevance Floor
 
 | Strategy | Combined | ILAD | ILMD | `diversity` |
 |----------|:--------:|:----:|:----:|:-----------:|
-| **DPP**  | **0.389** | 0.69 (+79%) | **0.31 (+178%)** | 0.9 |
-| SSD      | 0.254    | 0.60 (+52%) | 0.23 (+88%)      | 0.9 |
-| MMR      | 0.247    | 0.57 (+41%) | 0.24 (+97%)      | 0.8 |
-| MSD      | 0.228    | **0.68 (+71%)** | 0.20 (+47%)  | 0.5 |
+| **MMR**  | **0.298** | 0.61 (+45%) | **0.27 (+125%)** | 0.8 |
+| **DPP**  | 0.297    | 0.63 (+50%) | 0.26 (+117%)     | 0.8 |
+| SSD      | 0.254    | 0.60 (+43%) | 0.23 (+92%)      | 0.9 |
+| MSD      | 0.174    | **0.63 (+50%)** | 0.18 (+50%)  | 0.5 |
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (0-1 scale, higher = better). A score of 0.39 means ~39% of maximum possible balanced diversity improvement. Percentages show improvement over baseline (`diversity=0`). `diversity` shows the parameter value to achieve these results.*
+*Combined Score = geometric mean of normalized ILAD and ILMD gains (0-1 scale, higher = better). A score of 0.30 means ~30% of maximum possible balanced diversity improvement. Percentages show improvement over baseline (`diversity=0`). `diversity` shows the parameter value to achieve these results. Results averaged across 10 independent runs with different random seeds (std < 0.01 for all metrics).*
 
-**DPP wins at both thresholds** by balancing both ILAD (variety) and ILMD (worst-case diversity). At 95% floor, DPP achieves +79% ILAD and +178% ILMD improvement, a substantial diversity boost for at most 5% relevance cost.
+**DPP leads at 99% floor** with the best balanced diversity (0.274 vs next-best 0.195). At 95% floor, **MMR and DPP are essentially tied** (0.298 vs 0.297), with MMR having a slight edge on ILMD and DPP on ILAD.
 
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
-| **Best overall balance** | **DPP** | 0.7-0.9 | Wins both overall and ILMD |
+| **Best overall balance** | **DPP** or **MMR** | 0.7-0.8 | Essentially tied at 95% floor; DPP leads at 99% |
 | **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD while keeping relevance |
-| **Avoid similar pairs (ILMD)** | **DPP** | 0.7-0.9 | Best worst-case diversity (fewest near-duplicates) |
+| **Minimize similar pairs (ILMD)** | **DPP** or **MMR** | 0.7-0.8 | Both strong; DPP wins at strict floor |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
-| **Simple baseline** | **MMR** | 0.7-0.8 | Easy to implement, competitive |
+| **Simple implementation** | **MMR** | 0.7-0.8 | Easiest to implement, competitive results |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
@@ -250,6 +242,7 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 ### Experimental Setup
 
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
+- **Runs**: 10 runs per dataset with different random seeds for robust results (20,000 total evaluations)
 - **Embeddings**: 64-dim SVD on item co-occurrence matrix
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=10 items selected by each strategy
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index eef9365..a163be0 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -78,16 +78,35 @@ def cmd_download() -> None:
     logger.info("Download complete")
 
 
-def cmd_run() -> None:
+def cmd_run(n_runs: int = 5, overwrite: bool = False) -> None:
     """Run benchmarks on all datasets."""
-    logger.info("Running benchmark suite...")
+    logger.info(f"Running benchmark suite ({n_runs} runs per dataset for robustness)...")
 
     for name, info in DATASET_REGISTRY.items():
         logger.info(f"Benchmarking {name}...")
 
+        # Check if we should skip/resume
+        output_path = RESULTS_DIR / f"{name}.json"
+        if output_path.exists() and not overwrite:
+            import json
+
+            with open(output_path) as f:
+                existing = json.load(f)
+            existing_runs = len(existing.get("per_run_results", {}))
+            if existing_runs >= n_runs:
+                logger.info(f"Skipping {name}: already has {existing_runs} runs (use --overwrite for fresh)")
+                continue
+            elif existing_runs > 0:
+                logger.info(f"Resuming {name}: {existing_runs}/{n_runs} runs completed")
+
+        if overwrite and output_path.exists():
+            output_path.unlink()
+            logger.info(f"Removed existing results for {name}")
+
         config = BenchmarkConfig(
             dataset=name,
             rating_threshold=info.rating_threshold,
+            n_runs=n_runs,
         )
 
         try:
@@ -125,20 +144,31 @@ def main() -> None:
         default=None,
         help="Command to run (default: run)",
     )
+    parser.add_argument(
+        "--n-runs",
+        type=int,
+        default=5,
+        help="Number of runs per dataset for statistical robustness (default: 5)",
+    )
+    parser.add_argument(
+        "--overwrite",
+        action="store_true",
+        help="Overwrite existing results instead of resuming",
+    )
 
     args = parser.parse_args()
 
     if args.command == "download":
         cmd_download()
     elif args.command == "run":
-        cmd_run()
+        cmd_run(n_runs=args.n_runs, overwrite=args.overwrite)
     elif args.command == "report":
         cmd_report()
     elif args.command == "latency":
         cmd_latency()
     else:
         # Default: run
-        cmd_run()
+        cmd_run(n_runs=args.n_runs, overwrite=args.overwrite)
 
 
 if __name__ == "__main__":
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index 75b44d6..2633a5d 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -37,6 +37,7 @@ class BenchmarkConfig:
 
     # Reproducibility
     seed: int = 42
+    n_runs: int = 1  # Number of runs with different seeds for robustness
 
     def __post_init__(self) -> None:
         """Post-initialization processing."""
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index 1290409..fd29131 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -4,6 +4,7 @@
 import logging
 from collections import defaultdict
 from datetime import datetime, timezone
+from pathlib import Path
 
 import numpy as np
 import pyversity
@@ -24,9 +25,20 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
         msg = "config.dataset must be specified"
         raise ValueError(msg)
 
-    rng = np.random.default_rng(config.seed)
+    dataset_name = config.dataset if isinstance(config.dataset, str) else config.dataset.name
+    config.output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = config.output_dir / f"{dataset_name}.json"
 
-    # Load and prepare data
+    # Load existing results if resuming
+    existing_runs: dict[int, list[dict]] = {}
+    if output_path.exists():
+        with open(output_path) as f:
+            existing = json.load(f)
+        if "per_run_results" in existing:
+            existing_runs = {int(k): v for k, v in existing["per_run_results"].items()}
+            logger.info(f"Resuming: found {len(existing_runs)} existing runs")
+
+    # Load and prepare data (once, shared across runs)
     logger.debug("[1/4] Loading dataset...")
     data = load_dataset(config.dataset, config.min_interactions, config.rating_threshold)
 
@@ -37,23 +49,40 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
     similarity = compute_similarity_matrix(embeddings, top_k=100)
     logger.debug(f"Similarity matrix: {similarity.shape}, nnz={similarity.nnz:,}")
 
-    # Sample users
-    user_counts = np.bincount(data.user_ids, minlength=data.n_users)
-    eligible = np.where(user_counts >= 2)[0]
-    sampled = rng.choice(eligible, min(len(eligible), config.sample_users), replace=False)
-    logger.debug(f"[4/4] Evaluating {len(sampled)} users...")
+    # Run multiple times with different seeds for robustness
+    per_run_results: dict[int, list[dict]] = existing_runs.copy()
 
-    # Run evaluation
-    all_results = []
-    for user_id in tqdm(sampled, desc="Users"):
-        user_results = _evaluate_user(user_id, data, embeddings, similarity, config, rng)
-        all_results.extend(user_results)
+    for run_idx in range(config.n_runs):
+        # Skip if already completed
+        if run_idx in per_run_results:
+            logger.info(f"Skipping run {run_idx + 1}/{config.n_runs} (already completed)")
+            continue
 
-    # Aggregate results
-    aggregated = _aggregate_results(all_results, config)
+        run_seed = config.seed + run_idx
+        rng = np.random.default_rng(run_seed)
 
-    # Build output
-    dataset_name = config.dataset if isinstance(config.dataset, str) else config.dataset.name
+        # Sample users (different sample per run)
+        user_counts = np.bincount(data.user_ids, minlength=data.n_users)
+        eligible = np.where(user_counts >= 2)[0]
+        sampled = rng.choice(eligible, min(len(eligible), config.sample_users), replace=False)
+
+        run_desc = f"Run {run_idx + 1}/{config.n_runs}" if config.n_runs > 1 else "Users"
+        logger.debug(f"[4/4] {run_desc}: Evaluating {len(sampled)} users...")
+
+        # Run evaluation for this run
+        run_results = []
+        for user_id in tqdm(sampled, desc=run_desc):
+            user_results = _evaluate_user(user_id, data, embeddings, similarity, config, rng)
+            run_results.extend(user_results)
+
+        # Save this run immediately (for resumability)
+        per_run_results[run_idx] = _aggregate_single_run(run_results)
+        _save_intermediate(output_path, dataset_name, data, config, per_run_results)
+
+    # Aggregate results across all runs
+    aggregated = _aggregate_across_runs(per_run_results)
+
+    # Build final output
     output = {
         "dataset": dataset_name,
         "timestamp": datetime.now(timezone.utc).isoformat(),
@@ -61,17 +90,18 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
             "n_users": data.n_users,
             "n_items": data.n_items,
             "n_interactions": len(data.user_ids),
-            "sample_users": len(sampled),
+            "sample_users": config.sample_users,
+            "n_runs": config.n_runs,
+            "total_evaluations": config.sample_users * config.n_runs,
             "k": config.k,
             "embedding_dim": config.embedding_dim,
             "seed": config.seed,
         },
+        "per_run_results": {str(k): v for k, v in per_run_results.items()},
         "results": aggregated,
     }
 
-    # Save
-    config.output_dir.mkdir(parents=True, exist_ok=True)
-    output_path = config.output_dir / f"{dataset_name}.json"
+    # Save final results
     with open(output_path, "w") as f:
         json.dump(output, f, indent=2)
     logger.debug(f"Saved results to: {output_path}")
@@ -82,6 +112,31 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
     return output
 
 
+def _save_intermediate(
+    output_path: Path, dataset_name: str, data: InteractionData, config: BenchmarkConfig, per_run_results: dict
+) -> None:
+    """Save intermediate results after each run for resumability."""
+    intermediate = {
+        "dataset": dataset_name,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "config": {
+            "n_users": data.n_users,
+            "n_items": data.n_items,
+            "n_interactions": len(data.user_ids),
+            "sample_users": config.sample_users,
+            "n_runs": config.n_runs,
+            "k": config.k,
+            "embedding_dim": config.embedding_dim,
+            "seed": config.seed,
+        },
+        "per_run_results": {str(k): v for k, v in per_run_results.items()},
+        "results": _aggregate_across_runs(per_run_results),
+    }
+    with open(output_path, "w") as f:
+        json.dump(intermediate, f, indent=2)
+    logger.debug(f"Saved intermediate results ({len(per_run_results)} runs completed)")
+
+
 def _evaluate_user(
     user_id: int,
     data: InteractionData,
@@ -139,8 +194,8 @@ def _evaluate_user(
     return results
 
 
-def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dict]:
-    """Aggregate per-user results by strategy/diversity into mean and std."""
+def _aggregate_single_run(results: list[dict]) -> list[dict]:
+    """Aggregate per-user results from a single run into means."""
     groups: dict[tuple[str, float], list[dict]] = defaultdict(list)
     for r in results:
         key = (r["strategy"], r["diversity"])
@@ -152,6 +207,31 @@ def _aggregate_results(results: list[dict], config: BenchmarkConfig) -> list[dic
         for metric in ["mrr", "ndcg@10", "ilad", "ilmd"]:
             values = [r[metric] for r in group]
             agg[metric] = float(np.mean(values))
+        aggregated.append(agg)
+
+    return aggregated
+
+
+def _aggregate_across_runs(per_run_results: dict[int, list[dict]]) -> list[dict]:
+    """Aggregate results across multiple runs, computing mean and std."""
+    if not per_run_results:
+        return []
+
+    # Group by (strategy, diversity) across runs
+    groups: dict[tuple[str, float], dict[str, list[float]]] = defaultdict(lambda: defaultdict(list))
+
+    for run_results in per_run_results.values():
+        for row in run_results:
+            key = (row["strategy"], row["diversity"])
+            for metric in ["mrr", "ndcg@10", "ilad", "ilmd"]:
+                groups[key][metric].append(row[metric])
+
+    aggregated = []
+    for (strategy, diversity), metrics in sorted(groups.items()):
+        agg = {"strategy": strategy, "diversity": diversity}
+        for metric in ["mrr", "ndcg@10", "ilad", "ilmd"]:
+            values = metrics[metric]
+            agg[metric] = float(np.mean(values))
             agg[f"{metric}_std"] = float(np.std(values))
         aggregated.append(agg)
 
diff --git a/benchmarks/results/amazon-video-games.json b/benchmarks/results/amazon-video-games.json
index 3cb5acb..9361f3c 100644
--- a/benchmarks/results/amazon-video-games.json
+++ b/benchmarks/results/amazon-video-games.json
@@ -1,543 +1,4087 @@
 {
   "dataset": "amazon-video-games",
-  "timestamp": "2026-01-24T18:55:43.856497+00:00",
+  "timestamp": "2026-01-25T11:44:58.210613+00:00",
   "config": {
     "n_users": 707,
     "n_items": 537,
     "n_interactions": 5834,
-    "sample_users": 707,
+    "sample_users": 2000,
+    "n_runs": 10,
+    "total_evaluations": 20000,
     "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
+  "per_run_results": {
+    "0": [
+      {
+        "strategy": "dpp",
+        "diversity": 0.0,
+        "mrr": 0.23228822882288228,
+        "ndcg@10": 0.2790212189683253,
+        "ilad": 0.7105413888181958,
+        "ilmd": 0.2455601963666406
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.1,
+        "mrr": 0.24022080779506522,
+        "ndcg@10": 0.2891892107146568,
+        "ilad": 0.7398682728599456,
+        "ilmd": 0.29090181899374246
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.2,
+        "mrr": 0.24096338205249096,
+        "ndcg@10": 0.2904184030580189,
+        "ilad": 0.7431993081512344,
+        "ilmd": 0.295381149780295
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.3,
+        "mrr": 0.24146797332794506,
+        "ndcg@10": 0.2914447745878532,
+        "ilad": 0.7475329471865334,
+        "ilmd": 0.30226651605487376
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.4,
+        "mrr": 0.2418131609079275,
+        "ndcg@10": 0.29174199036871673,
+        "ilad": 0.753077340471694,
+        "ilmd": 0.3115877307624763
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.5,
+        "mrr": 0.24255012235917472,
+        "ndcg@10": 0.2918043722695043,
+        "ilad": 0.7614449542094828,
+        "ilmd": 0.32528375609760746
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.6,
+        "mrr": 0.2437563144069509,
+        "ndcg@10": 0.2937245596859424,
+        "ilad": 0.77179579207044,
+        "ilmd": 0.3439874860656986
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.7,
+        "mrr": 0.24636907568307853,
+        "ndcg@10": 0.2965082931174429,
+        "ilad": 0.7880115334097364,
+        "ilmd": 0.37037678376957245
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.8,
+        "mrr": 0.24790642329539075,
+        "ndcg@10": 0.29818124085336956,
+        "ilad": 0.8174925586458985,
+        "ilmd": 0.4172500453710219
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.9,
+        "mrr": 0.2532077187310568,
+        "ndcg@10": 0.30604162531719953,
+        "ilad": 0.875048992573119,
+        "ilmd": 0.5217687364514849
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 1.0,
+        "mrr": 0.16760344401787117,
+        "ndcg@10": 0.17105421732221834,
+        "ilad": 0.9902785942274577,
+        "ilmd": 0.8856152248011621
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.0,
+        "mrr": 0.23228822882288228,
+        "ndcg@10": 0.2790212189683253,
+        "ilad": 0.7105413888181958,
+        "ilmd": 0.2455601963666406
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.1,
+        "mrr": 0.2361711681372219,
+        "ndcg@10": 0.284621131411053,
+        "ilad": 0.7203397834317856,
+        "ilmd": 0.2571816031295474
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.2,
+        "mrr": 0.23962136009519322,
+        "ndcg@10": 0.2892952684375146,
+        "ilad": 0.7316462977182072,
+        "ilmd": 0.2730726538149606
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.3,
+        "mrr": 0.24307716485934308,
+        "ndcg@10": 0.2937064016278286,
+        "ilad": 0.7445790509692007,
+        "ilmd": 0.2977379679342645
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.4,
+        "mrr": 0.24520554096225952,
+        "ndcg@10": 0.2961226036574152,
+        "ilad": 0.7619860979927311,
+        "ilmd": 0.32374756879145555
+      },
+      {
+        "strategy": "mmr",
+        "diversity": 0.5,
+        "mrr": 0.24744561190812958,
+        "ndcg@10": 0.2992327071417331,
+        "ilad": 0.788008840451652,
+        "ilmd": 0.364086535223134
+      },
+      {
+        "strategy": "mmr",
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+        "ndcg@10": 0.30368781544104784,
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+        "ilmd": 0.42638554195366285
+      },
+      {
+        "strategy": "mmr",
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+        "ilad": 0.8808217485352318,
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+        "ndcg@10": 0.20638869933797016,
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+        "ndcg@10": 0.2814293752101563,
+        "ilad": 0.7170515359291093,
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+        "ilad": 0.7253163911749989,
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+        "ilad": 0.733628064351844,
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+        "ndcg@10": 0.2911381235802141,
+        "ilad": 0.7436758087581758,
+        "ilmd": 0.28926380975580285
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+        "ilad": 0.8812638247198566,
+        "ilmd": 0.4920155907621478
+      },
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+        "ilad": 1.0137492790923597,
+        "ilmd": 0.8680071153768892
+      }
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+    "1": [
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+        "ilad": 0.7383286998076352,
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+        "ilad": 0.7418690306125301,
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+        "ilad": 0.7462987256100694,
+        "ilmd": 0.3029398843962199
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+        "ndcg@10": 0.3090376535375595,
+        "ilad": 0.7522447596935099,
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+      },
+      {
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+        "diversity": 0.5,
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+        "ndcg@10": 0.30885607232589884,
+        "ilad": 0.760533625536626,
+        "ilmd": 0.32322071667607805
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.6,
+        "mrr": 0.2590141667227947,
+        "ndcg@10": 0.3106242592164348,
+        "ilad": 0.7727743958398341,
+        "ilmd": 0.3427023099369173
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.7,
+        "mrr": 0.26369131811140295,
+        "ndcg@10": 0.3177103421824238,
+        "ilad": 0.7894754342344896,
+        "ilmd": 0.3733161260417361
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.8,
+        "mrr": 0.2624851260636268,
+        "ndcg@10": 0.31461692998831375,
+        "ilad": 0.8170257047177035,
+        "ilmd": 0.4166105854966516
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.9,
+        "mrr": 0.26218820861678005,
+        "ndcg@10": 0.3095025977977347,
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 }
diff --git a/benchmarks/results/goodreads.json b/benchmarks/results/goodreads.json
index 7875a90..e1b0fc1 100644
--- a/benchmarks/results/goodreads.json
+++ b/benchmarks/results/goodreads.json
@@ -1,543 +1,4087 @@
 {
   "dataset": "goodreads",
-  "timestamp": "2026-01-24T18:56:34.142616+00:00",
+  "timestamp": "2026-01-25T11:53:46.867538+00:00",
   "config": {
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     "n_items": 20980,
     "n_interactions": 561514,
     "sample_users": 2000,
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+    "total_evaluations": 20000,
     "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
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diff --git a/benchmarks/results/lastfm.json b/benchmarks/results/lastfm.json
index 0407085..cbfd171 100644
--- a/benchmarks/results/lastfm.json
+++ b/benchmarks/results/lastfm.json
@@ -1,543 +1,4087 @@
 {
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+  "timestamp": "2026-01-25T11:43:46.869133+00:00",
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diff --git a/benchmarks/results/ml-32m.json b/benchmarks/results/ml-32m.json
index 01408fe..e54c6cf 100644
--- a/benchmarks/results/ml-32m.json
+++ b/benchmarks/results/ml-32m.json
@@ -1,543 +1,4087 @@
 {
   "dataset": "ml-32m",
-  "timestamp": "2026-01-24T18:55:04.347930+00:00",
+  "timestamp": "2026-01-25T11:37:23.521317+00:00",
   "config": {
     "n_users": 198973,
     "n_items": 25051,
     "n_interactions": 15880655,
     "sample_users": 2000,
+    "n_runs": 10,
+    "total_evaluations": 20000,
     "k": 10,
     "embedding_dim": 64,
     "seed": 42
   },
+  "per_run_results": {
+    "0": [
+      {
+        "strategy": "dpp",
+        "diversity": 0.0,
+        "mrr": 0.03724742063492063,
+        "ndcg@10": 0.05471859661567804,
+        "ilad": 0.2921844469467178,
+        "ilmd": 0.06625440615415573
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.1,
+        "mrr": 0.04122857142857143,
+        "ndcg@10": 0.06107098204808578,
+        "ilad": 0.3437053569192067,
+        "ilmd": 0.09041877481341362
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.2,
+        "mrr": 0.041417857142857145,
+        "ndcg@10": 0.061449460797096,
+        "ilad": 0.3505621356749907,
+        "ilmd": 0.09279653978347778
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.3,
+        "mrr": 0.04152777777777778,
+        "ndcg@10": 0.06145262661661947,
+        "ilad": 0.35819286767113956,
+        "ilmd": 0.09628867974877357
+      },
+      {
+        "strategy": "dpp",
+        "diversity": 0.4,
+        "mrr": 0.04197400793650794,
+        "ndcg@10": 0.062014620290202535,
+        "ilad": 0.37036212904937565,
+        "ilmd": 0.1014302430152893
+      },
+      {
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+        "mrr": 0.04226904761904761,
+        "ndcg@10": 0.06271219555682002,
+        "ilad": 0.38554022964462636,
+        "ilmd": 0.10760816338658333
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diff --git a/benchmarks/results/pareto_ilad.png b/benchmarks/results/pareto_ilad.png
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From be1a702629a5a6a8436f2b3bd2c174addfe4259c Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 25 Jan 2026 13:33:36 +0100
Subject: [PATCH 46/69] Switched to using ndcg as main metric

---
 benchmarks/README.md           | 153 ++++++------
 benchmarks/core/report.py      | 416 ++++++++++++++++++++++++---------
 benchmarks/results/latency.png | Bin 80884 -> 80442 bytes
 3 files changed, 396 insertions(+), 173 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1b039b1..3c0aa0f 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -27,45 +27,61 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 We measure two diversity metrics: **ILAD** (average pairwise diversity—higher = more variety) and **ILMD** (minimum pairwise diversity—higher = fewer similar pairs).
 
-We use a **relevance-budgeted** approach: for each strategy, we find the best config that maintains a minimum percentage of baseline relevance (see [Methodology](#methodology) for details).
+### Key Insight: Diversification Can *Improve* Relevance
+
+A surprising finding: moderate diversification often **improves** relevance (nDCG), not just diversity. This happens because diversifiers act as intelligent tie-breakers, helping surface items from different preference modes.
 
 ### Overall Results
 
-We report results at two relevance floors:
-- **99%**: Near-zero relevance loss, production-safe
-- **95%**: Balanced tradeoff with more diversity headroom
+Each strategy's **sweet spot**—the best operating point that maintains or improves relevance:
+
+| Strategy | `diversity` | nDCG Δ | ILAD (+%) | ILMD (+%) |
+|----------|:-----------:|:------:|:---------:|:---------:|
+| **DPP**  | 0.8 | **+3.9%** | +47% | **+101%** |
+| SSD      | 0.8 | +3.1% | +34% | +60% |
+| MMR      | 0.7 | +2.1% | +20% | +51% |
+| MSD      | 0.3 | +1.2% | +33% | +18% |
 
-#### 99% Relevance Floor
+*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Sweet spot = highest diversity while improving or maintaining relevance. Results averaged across 4 datasets, 10 runs.*
 
-| Strategy | Combined | ILAD | ILMD | `diversity` |
-|----------|:--------:|:----:|:----:|:-----------:|
-| **DPP**  | **0.274** | 0.60 (+43%) | **0.25 (+108%)** | 0.8 |
-| SSD      | 0.195    | 0.55 (+31%) | 0.21 (+75%)      | 0.8 |
-| MMR      | 0.189    | 0.53 (+26%) | 0.22 (+83%)      | 0.7 |
-| MSD      | 0.117    | **0.57 (+36%)** | 0.16 (+33%)  | 0.3 |
+**DPP leads overall**, achieving +3.9% nDCG improvement while also boosting ILAD by 47% and ILMD by 101%—a true "free lunch."
 
-#### 95% Relevance Floor
+<details>
+<summary>Results at Specific Relevance Floors</summary>
 
-| Strategy | Combined | ILAD | ILMD | `diversity` |
-|----------|:--------:|:----:|:----:|:-----------:|
-| **MMR**  | **0.298** | 0.61 (+45%) | **0.27 (+125%)** | 0.8 |
-| **DPP**  | 0.297    | 0.63 (+50%) | 0.26 (+117%)     | 0.8 |
-| SSD      | 0.254    | 0.60 (+43%) | 0.23 (+92%)      | 0.9 |
-| MSD      | 0.174    | **0.63 (+50%)** | 0.18 (+50%)  | 0.5 |
+These tables show results when you allow some relevance loss (vs the sweet spot above which requires no loss):
 
-*Combined Score = geometric mean of normalized ILAD and ILMD gains (0-1 scale, higher = better). A score of 0.30 means ~30% of maximum possible balanced diversity improvement. Percentages show improvement over baseline (`diversity=0`). `diversity` shows the parameter value to achieve these results. Results averaged across 10 independent runs with different random seeds (std < 0.01 for all metrics).*
+#### 99% Relevance Floor (≤1% loss allowed)
 
-**DPP leads at 99% floor** with the best balanced diversity (0.274 vs next-best 0.195). At 95% floor, **MMR and DPP are essentially tied** (0.298 vs 0.297), with MMR having a slight edge on ILMD and DPP on ILAD.
+| Strategy | `diversity` | nDCG Δ | ILAD | ILMD |
+|----------|:-----------:|:------:|:----:|:----:|
+| **DPP**  | 0.8 | **+3.7%** | 0.60 (+51%) | **0.25 (+107%)** |
+| SSD      | 0.8 | +3.1% | 0.55 (+34%) | 0.21 (+60%) |
+| MMR      | 0.7 | +1.8% | 0.53 (+26%) | 0.22 (+61%) |
+| MSD      | 0.3 | +0.8% | 0.57 (+39%) | 0.16 (+20%) |
+
+#### 95% Relevance Floor (≤5% loss allowed)
+
+| Strategy | `diversity` | nDCG Δ | ILAD | ILMD |
+|----------|:-----------:|:------:|:----:|:----:|
+| **DPP**  | 0.8 | **+2.8%** | 0.63 (+58%) | **0.26 (+117%)** |
+| MMR      | 0.8 | -2.4% | 0.61 (+53%) | 0.27 (+122%) |
+| SSD      | 0.9 | +0.4% | 0.60 (+52%) | 0.23 (+88%) |
+| MSD      | 0.5 | -1.6% | 0.63 (+57%) | 0.18 (+32%) |
+
+*With a looser floor, you can push diversity higher—but DPP still leads with positive nDCG gains.*
+
+</details>
 
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
-| **Best overall balance** | **DPP** or **MMR** | 0.7-0.8 | Essentially tied at 95% floor; DPP leads at 99% |
-| **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD while keeping relevance |
-| **Minimize similar pairs (ILMD)** | **DPP** or **MMR** | 0.7-0.8 | Both strong; DPP wins at strict floor |
+| **Best overall** | **DPP** | 0.7-0.8 | Best combined score; improves relevance AND diversity |
+| **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD, but worse ILMD and may lose relevance |
+| **Minimize similar pairs (ILMD)** | **DPP** | 0.7-0.8 | Best ILMD with strong ILAD too |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
-| **Simple implementation** | **MMR** | 0.7-0.8 | Easiest to implement, competitive results |
+| **Simple implementation** | **MMR** | 0.7-0.8 | Easiest to implement, good results |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
@@ -163,64 +179,64 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 | `diversity` | nDCG Retention | ILAD | ILMD |
 |:-----------:|:--------------:|:----:|:----:|
 | 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
-| 0.1         | 100.9%          | 0.42 (+1%) | 0.13 (+2%) |
-| 0.2         | 101.7%          | 0.43 (+3%) | 0.13 (+5%) |
-| 0.3         | 102.9%          | 0.44 (+4%) | 0.14 (+9%) |
-| 0.4         | 103.4%          | 0.45 (+7%) | 0.15 (+15%) |
-| 0.5         | 103.0%          | 0.46 (+10%) | 0.16 (+23%) |
-| 0.6         | 103.5%          | 0.48 (+16%) | 0.18 (+36%) |
-| 0.7         | 103.1%          | 0.52 (+24%) | 0.22 (+60%) |
-| 0.8         | 98.1%          | 0.58 (+41%) | 0.28 (+110%) |
-| 0.9         | 85.0%          | 0.71 (+79%) | 0.42 (+247%) |
-| 1.0         | 46.3%          | 0.92 (+142%) | 0.76 (+655%) |
+| 0.1         | 100.6%          | 0.42 (+1%) | 0.13 (+2%) |
+| 0.2         | 101.0%          | 0.43 (+3%) | 0.13 (+5%) |
+| 0.3         | 101.5%          | 0.44 (+4%) | 0.14 (+9%) |
+| 0.4         | 101.7%          | 0.45 (+7%) | 0.15 (+15%) |
+| 0.5         | 102.0%          | 0.46 (+10%) | 0.16 (+23%) |
+| 0.6         | 102.2%          | 0.48 (+16%) | 0.18 (+36%) |
+| 0.7         | 100.7%          | 0.52 (+25%) | 0.22 (+61%) |
+| 0.8         | 95.5%          | 0.58 (+41%) | 0.28 (+111%) |
+| 0.9         | 82.4%          | 0.71 (+79%) | 0.42 (+247%) |
+| 1.0         | 48.0%          | 0.92 (+142%) | 0.76 (+656%) |
 
 #### MSD
 
 | `diversity` | nDCG Retention | ILAD | ILMD |
 |:-----------:|:--------------:|:----:|:----:|
 | 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
-| 0.1         | 102.9%          | 0.46 (+10%) | 0.13 (+5%) |
-| 0.2         | 103.2%          | 0.50 (+22%) | 0.14 (+11%) |
-| 0.3         | 103.1%          | 0.55 (+34%) | 0.16 (+19%) |
-| 0.4         | 101.6%          | 0.60 (+48%) | 0.17 (+30%) |
-| 0.5         | 98.1%          | 0.66 (+65%) | 0.20 (+45%) |
-| 0.6         | 92.0%          | 0.72 (+84%) | 0.23 (+64%) |
-| 0.7         | 82.7%          | 0.80 (+107%) | 0.26 (+93%) |
-| 0.8         | 72.6%          | 0.87 (+129%) | 0.31 (+138%) |
-| 0.9         | 56.7%          | 0.93 (+145%) | 0.40 (+224%) |
-| 1.0         | 43.4%          | 0.95 (+153%) | 0.53 (+373%) |
+| 0.1         | 101.5%          | 0.46 (+11%) | 0.13 (+4%) |
+| 0.2         | 101.6%          | 0.50 (+22%) | 0.14 (+11%) |
+| 0.3         | 100.9%          | 0.55 (+34%) | 0.16 (+19%) |
+| 0.4         | 98.6%          | 0.60 (+48%) | 0.17 (+30%) |
+| 0.5         | 94.9%          | 0.66 (+64%) | 0.20 (+45%) |
+| 0.6         | 89.4%          | 0.72 (+84%) | 0.23 (+65%) |
+| 0.7         | 81.3%          | 0.80 (+107%) | 0.26 (+92%) |
+| 0.8         | 71.4%          | 0.87 (+129%) | 0.31 (+137%) |
+| 0.9         | 57.7%          | 0.93 (+145%) | 0.40 (+225%) |
+| 1.0         | 45.4%          | 0.95 (+152%) | 0.53 (+374%) |
 
 #### DPP
 
 | `diversity` | nDCG Retention | ILAD | ILMD |
 |:-----------:|:--------------:|:----:|:----:|
 | 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
-| 0.1         | 104.8%          | 0.46 (+13%) | 0.15 (+26%) |
-| 0.2         | 105.2%          | 0.47 (+15%) | 0.15 (+29%) |
-| 0.3         | 105.2%          | 0.48 (+17%) | 0.16 (+33%) |
-| 0.4         | 105.2%          | 0.49 (+19%) | 0.16 (+38%) |
-| 0.5         | 105.1%          | 0.50 (+24%) | 0.17 (+46%) |
-| 0.6         | 105.2%          | 0.52 (+30%) | 0.19 (+57%) |
-| 0.7         | 106.9%          | 0.56 (+41%) | 0.21 (+77%) |
-| 0.8         | 103.0%          | 0.64 (+65%) | 0.25 (+118%) |
-| 0.9         | 92.4%          | 0.80 (+111%) | 0.39 (+275%) |
-| 1.0         | 44.6%          | 0.92 (+144%) | 0.74 (+623%) |
+| 0.1         | 102.6%          | 0.47 (+13%) | 0.15 (+26%) |
+| 0.2         | 103.0%          | 0.47 (+15%) | 0.15 (+29%) |
+| 0.3         | 103.3%          | 0.48 (+17%) | 0.16 (+33%) |
+| 0.4         | 103.6%          | 0.49 (+19%) | 0.16 (+38%) |
+| 0.5         | 103.7%          | 0.50 (+23%) | 0.17 (+46%) |
+| 0.6         | 103.9%          | 0.52 (+30%) | 0.18 (+57%) |
+| 0.7         | 104.2%          | 0.56 (+41%) | 0.21 (+77%) |
+| 0.8         | 101.6%          | 0.64 (+65%) | 0.25 (+118%) |
+| 0.9         | 88.1%          | 0.80 (+111%) | 0.39 (+275%) |
+| 1.0         | 47.3%          | 0.92 (+144%) | 0.74 (+624%) |
 
 #### SSD
 
 | `diversity` | nDCG Retention | ILAD | ILMD |
 |:-----------:|:--------------:|:----:|:----:|
 | 0.0         | 100.0%          | 0.42 (+0%) | 0.12 (+0%) |
-| 0.1         | 100.7%          | 0.42 (+1%) | 0.12 (+1%) |
-| 0.2         | 101.5%          | 0.43 (+2%) | 0.13 (+2%) |
-| 0.3         | 102.4%          | 0.43 (+3%) | 0.13 (+4%) |
-| 0.4         | 102.7%          | 0.43 (+4%) | 0.13 (+6%) |
-| 0.5         | 104.2%          | 0.44 (+6%) | 0.14 (+10%) |
-| 0.6         | 105.6%          | 0.46 (+10%) | 0.14 (+16%) |
-| 0.7         | 105.7%          | 0.48 (+16%) | 0.15 (+25%) |
-| 0.8         | 106.5%          | 0.53 (+31%) | 0.18 (+47%) |
-| 0.9         | 92.6%          | 0.76 (+98%) | 0.29 (+146%) |
-| 1.0         | 44.0%          | 0.94 (+149%) | 0.73 (+611%) |
+| 0.1         | 100.3%          | 0.42 (+1%) | 0.12 (+1%) |
+| 0.2         | 100.7%          | 0.43 (+2%) | 0.13 (+2%) |
+| 0.3         | 101.2%          | 0.43 (+3%) | 0.13 (+4%) |
+| 0.4         | 101.6%          | 0.44 (+4%) | 0.13 (+6%) |
+| 0.5         | 102.3%          | 0.44 (+6%) | 0.14 (+10%) |
+| 0.6         | 103.1%          | 0.46 (+10%) | 0.14 (+16%) |
+| 0.7         | 103.9%          | 0.48 (+16%) | 0.15 (+25%) |
+| 0.8         | 103.9%          | 0.53 (+31%) | 0.18 (+47%) |
+| 0.9         | 90.7%          | 0.76 (+97%) | 0.29 (+147%) |
+| 1.0         | 46.5%          | 0.94 (+148%) | 0.72 (+612%) |
 
 *Percentages show gain vs baseline (diversity=0).*
 
@@ -257,18 +273,19 @@ We use a **relevance floor** approach to ensure fair comparison:
 3. **Compare**: Among feasible configs, find which strategy achieves:
    - **Max ILAD**: Best overall diversity spread
    - **Max ILMD**: Best worst-case diversity (fewer similar pairs)
-   - **Best Combined**: Geometric mean of normalized ILAD and ILMD gains
+   - **Best Overall**: Best 3-way score combining all metrics
 
 We report results at two relevance floors:
 - **99%**: Near-zero relevance loss, production-safe
 - **95%**: Balanced tradeoff with more diversity headroom
 
-The **Combined Score** normalizes gains relative to baseline (`diversity=0`):
+The **3-way Score** rewards strategies that improve *all three* metrics:
+- `nDCG_gain = normalized(nDCG / baseline_nDCG)`
 - `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`
 - `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_max - ILMD_baseline)`
-- `Combined = sqrt(ILAD_gain × ILMD_gain)`
+- `Score = ∛(nDCG_gain × ILAD_gain × ILMD_gain)` (geometric mean)
 
-This ensures a strategy must improve *both* metrics to score well—high only if both improve.
+This ensures a strategy must improve relevance AND diversity to score well.
 
 ### Diversity Metrics
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index 69ea0a3..f0ff876 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -98,6 +98,7 @@ def _compute_relevance_budgeted_scores(
 
         # Goal 3: Best combined (geometric mean of normalized gains relative to baseline)
         def combined_score(point: dict) -> float:
+            """2-way score: geometric mean of ILAD and ILMD gains."""
             ilad_range = ilad_max - ilad_baseline
             ilmd_range = ilmd_max - ilmd_baseline
             if ilad_range == 0 or ilmd_range == 0:
@@ -107,6 +108,19 @@ def combined_score(point: dict) -> float:
             ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
             return (ilad_gain * ilmd_gain) ** 0.5  # Geometric mean
 
+        def combined_score_3way(point: dict) -> float:
+            """3-way score: includes nDCG gain (rewards relevance improvement)."""
+            ilad_range = ilad_max - ilad_baseline
+            ilmd_range = ilmd_max - ilmd_baseline
+            if ilad_range == 0 or ilmd_range == 0:
+                return 0.0
+            ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+            ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
+            # nDCG gain: ratio vs baseline, clamped to [0, 2] then normalized to [0, 1]
+            ndcg_ratio = point["ndcg"] / baseline_ndcg if baseline_ndcg > 0 else 1.0
+            ndcg_gain = max(0.0, min(1.0, (ndcg_ratio - 0.5) / 1.0))  # 0.5-1.5 -> 0-1
+            return (ilad_gain * ilmd_gain * ndcg_gain) ** (1 / 3)  # Cubic root for 3-way
+
         # Tie-break by higher nDCG, lower lambda
         best_combined = max(feasible, key=lambda p: (combined_score(p), p["ndcg"], -p["lambda"]))
         results[dataset]["best_combined"] = {
@@ -119,6 +133,18 @@ def combined_score(point: dict) -> float:
             "score": combined_score(best_combined),
         }
 
+        # Goal 4: Best 3-way score (includes nDCG improvement)
+        best_3way = max(feasible, key=lambda p: (combined_score_3way(p), p["ndcg"], -p["lambda"]))
+        results[dataset]["best_3way"] = {
+            "strategy": best_3way["strategy"],
+            "lambda": best_3way["lambda"],
+            "ndcg": best_3way["ndcg"],
+            "ndcg_vs_baseline": best_3way["ndcg"] / baseline_ndcg,
+            "ilad": best_3way["ilad"],
+            "ilmd": best_3way["ilmd"],
+            "score": combined_score_3way(best_3way),
+        }
+
     return results
 
 
@@ -154,21 +180,81 @@ def _get_dataset_baseline_and_bounds(
     return baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max
 
 
-def _compute_strategy_scorecard(all_data: list[dict], relevance_floor: float = 0.95) -> dict[str, dict[str, float]]:
+def _aggregate_strategy_metrics(strategy_data: dict[str, list[float]]) -> dict[str, float]:
+    """Aggregate per-dataset metrics into averages."""
+    return {key: sum(values) / len(values) if values else 0.0 for key, values in strategy_data.items()}
+
+
+def _compute_combined_score_2way(
+    point: dict, ilad_baseline: float, ilad_range: float, ilmd_baseline: float, ilmd_range: float
+) -> float:
+    """Compute 2-way combined score (ILAD × ILMD)."""
+    ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+    ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
+    return (ilad_gain * ilmd_gain) ** 0.5
+
+
+def _compute_combined_score_3way(
+    point: dict, baseline_ndcg: float, ilad_baseline: float, ilad_range: float, ilmd_baseline: float, ilmd_range: float
+) -> float:
+    """Compute 3-way combined score (nDCG × ILAD × ILMD)."""
+    ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+    ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
+    ndcg_ratio = point["ndcg"] / baseline_ndcg if baseline_ndcg > 0 else 1.0
+    ndcg_gain = max(0.0, min(1.0, (ndcg_ratio - 0.5) / 1.0))
+    return (ilad_gain * ilmd_gain * ndcg_gain) ** (1 / 3)
+
+
+def _update_accumulator(
+    acc: dict[str, list[float]],
+    point: dict,
+    baseline_ndcg: float,
+    ilad_baseline: float,
+    ilad_range: float,
+    ilmd_baseline: float,
+    ilmd_range: float,
+) -> None:
+    """Update accumulator with a point's metrics."""
+    acc["scores"].append(_compute_combined_score_2way(point, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range))
+    acc["scores_3way"].append(
+        _compute_combined_score_3way(point, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range)
+    )
+    acc["ilads"].append(point["ilad"])
+    acc["ilmds"].append(point["ilmd"])
+    acc["ndcg_ret"].append(point["ndcg"] / baseline_ndcg)
+    acc["lambdas"].append(point["lambda"])
+    if ilad_baseline > 0:
+        acc["ilad_pct"].append((point["ilad"] - ilad_baseline) / ilad_baseline * 100)
+    if ilmd_baseline > 0:
+        acc["ilmd_pct"].append((point["ilmd"] - ilmd_baseline) / ilmd_baseline * 100)
+    acc["ndcg_gain"].append((point["ndcg"] / baseline_ndcg - 1) * 100)
+
+
+def _compute_strategy_scorecard(
+    all_data: list[dict], relevance_floor: float | None = None
+) -> dict[str, dict[str, float]]:
     """
     Compute per-strategy aggregate scores across all datasets.
 
-    Returns combined score, ILAD/ILMD at best operating point, percentage gains vs baseline,
-    nDCG retention, and typical diversity.
+    If relevance_floor is None, finds the optimal point for each strategy (no constraint).
+    If relevance_floor is set, only considers points above that fraction of baseline nDCG.
     """
     datasets = sorted(set(p["dataset"] for p in all_data))
-    strategy_scores: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ilads: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ilmds: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ilad_pct_gains: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ilmd_pct_gains: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_ndcg_ret: dict[str, list[float]] = {s: [] for s in STRATEGIES}
-    strategy_lambdas: dict[str, list[float]] = {s: [] for s in STRATEGIES}
+
+    accumulators: dict[str, dict[str, list[float]]] = {
+        s: {
+            "scores": [],
+            "scores_3way": [],
+            "ilads": [],
+            "ilmds": [],
+            "ilad_pct": [],
+            "ilmd_pct": [],
+            "ndcg_ret": [],
+            "ndcg_gain": [],
+            "lambdas": [],
+        }
+        for s in STRATEGIES
+    }
 
     for dataset in datasets:
         dataset_points = [p for p in all_data if p["dataset"] == dataset]
@@ -177,62 +263,115 @@ def _compute_strategy_scorecard(all_data: list[dict], relevance_floor: float = 0
             continue
 
         baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max = bounds
-        ndcg_floor = relevance_floor * baseline_ndcg
         ilad_range = ilad_max - ilad_baseline
         ilmd_range = ilmd_max - ilmd_baseline
 
-        def combined_score(point: dict) -> float:
-            # Clamp gains to [0, 1] to handle noise/non-monotonic behavior
-            ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
-            ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
-            return (ilad_gain * ilmd_gain) ** 0.5
-
         for strategy in STRATEGIES:
             strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
-            feasible = [p for p in strategy_points if p["ndcg"] >= ndcg_floor]
+
+            if relevance_floor is not None:
+                ndcg_floor = relevance_floor * baseline_ndcg
+                feasible = [p for p in strategy_points if p["ndcg"] >= ndcg_floor]
+            else:
+                feasible = strategy_points
+
             if not feasible:
                 continue
 
-            # Select best point with tie-breaker
-            best_point = max(feasible, key=lambda p: (combined_score(p), p["ndcg"], -p["lambda"]))
-            strategy_scores[strategy].append(combined_score(best_point))
-            strategy_ilads[strategy].append(best_point["ilad"])
-            strategy_ilmds[strategy].append(best_point["ilmd"])
-            strategy_ndcg_ret[strategy].append(best_point["ndcg"] / baseline_ndcg)
-            strategy_lambdas[strategy].append(best_point["lambda"])
-
-            # Compute percentage gain over baseline
-            if ilad_baseline > 0:
-                ilad_pct_gain = (best_point["ilad"] - ilad_baseline) / ilad_baseline * 100
-                strategy_ilad_pct_gains[strategy].append(ilad_pct_gain)
-            if ilmd_baseline > 0:
-                ilmd_pct_gain = (best_point["ilmd"] - ilmd_baseline) / ilmd_baseline * 100
-                strategy_ilmd_pct_gains[strategy].append(ilmd_pct_gain)
+            best_point = max(
+                feasible,
+                key=lambda p: (
+                    _compute_combined_score_3way(
+                        p, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range
+                    ),
+                    p["ndcg"],
+                    -p["lambda"],
+                ),
+            )
+            _update_accumulator(
+                accumulators[strategy], best_point, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range
+            )
 
-    # Aggregate across datasets
     scorecard: dict[str, dict[str, float]] = {}
-    for strategy in STRATEGIES:
-        if strategy_scores[strategy]:
-            scores = strategy_scores[strategy]
-            ilads = strategy_ilads[strategy]
-            ilmds = strategy_ilmds[strategy]
-            ilad_pct = strategy_ilad_pct_gains[strategy]
-            ilmd_pct = strategy_ilmd_pct_gains[strategy]
-            ndcg_rets = strategy_ndcg_ret[strategy]
-            lambdas = strategy_lambdas[strategy]
+    for strategy, acc in accumulators.items():
+        if acc["scores"]:
             scorecard[strategy] = {
-                "combined_score": sum(scores) / len(scores),
-                "ilad_at_best": sum(ilads) / len(ilads),
-                "ilmd_at_best": sum(ilmds) / len(ilmds),
-                "ilad_pct_gain": sum(ilad_pct) / len(ilad_pct) if ilad_pct else 0.0,
-                "ilmd_pct_gain": sum(ilmd_pct) / len(ilmd_pct) if ilmd_pct else 0.0,
-                "ndcg_retention": sum(ndcg_rets) / len(ndcg_rets),
-                "typical_diversity": sum(lambdas) / len(lambdas),
+                "combined_score": sum(acc["scores"]) / len(acc["scores"]),
+                "combined_score_3way": sum(acc["scores_3way"]) / len(acc["scores_3way"]),
+                "ilad_at_best": sum(acc["ilads"]) / len(acc["ilads"]),
+                "ilmd_at_best": sum(acc["ilmds"]) / len(acc["ilmds"]),
+                "ilad_pct_gain": sum(acc["ilad_pct"]) / len(acc["ilad_pct"]) if acc["ilad_pct"] else 0.0,
+                "ilmd_pct_gain": sum(acc["ilmd_pct"]) / len(acc["ilmd_pct"]) if acc["ilmd_pct"] else 0.0,
+                "ndcg_retention": sum(acc["ndcg_ret"]) / len(acc["ndcg_ret"]),
+                "ndcg_pct_gain": sum(acc["ndcg_gain"]) / len(acc["ndcg_gain"]),
+                "typical_diversity": sum(acc["lambdas"]) / len(acc["lambdas"]),
             }
 
     return scorecard
 
 
+def _compute_sweet_spots(all_data: list[dict]) -> dict[str, dict[str, float]]:
+    """
+    Find the optimal operating point (sweet spot) for each strategy.
+
+    The sweet spot maximizes combined diversity while nDCG is at or above baseline.
+    This shows the "free lunch" region where you get diversity without losing relevance.
+    """
+    datasets = sorted(set(p["dataset"] for p in all_data))
+    strategy_sweet_spots: dict[str, list[dict]] = {s: [] for s in STRATEGIES}
+
+    for dataset in datasets:
+        dataset_points = [p for p in all_data if p["dataset"] == dataset]
+        bounds = _get_dataset_baseline_and_bounds(dataset_points)
+        if bounds is None:
+            continue
+
+        baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max = bounds
+        ilad_range = ilad_max - ilad_baseline
+        ilmd_range = ilmd_max - ilmd_baseline
+
+        def combined_diversity(point: dict) -> float:
+            ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
+            ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
+            return (ilad_gain * ilmd_gain) ** 0.5
+
+        for strategy in STRATEGIES:
+            strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
+            # Find points where nDCG >= baseline (no loss or gain)
+            no_loss = [p for p in strategy_points if p["ndcg"] >= baseline_ndcg]
+
+            if no_loss:
+                # Find point with best diversity that doesn't lose relevance
+                best = max(no_loss, key=lambda p: (combined_diversity(p), p["ndcg"], -p["lambda"]))
+                strategy_sweet_spots[strategy].append(
+                    {
+                        "lambda": best["lambda"],
+                        "ndcg_gain": (best["ndcg"] / baseline_ndcg - 1) * 100,
+                        "ilad": best["ilad"],
+                        "ilmd": best["ilmd"],
+                        "ilad_gain": (best["ilad"] - ilad_baseline) / ilad_baseline * 100 if ilad_baseline > 0 else 0,
+                        "ilmd_gain": (best["ilmd"] - ilmd_baseline) / ilmd_baseline * 100 if ilmd_baseline > 0 else 0,
+                    }
+                )
+
+    # Aggregate across datasets
+    sweet_spots: dict[str, dict[str, float]] = {}
+    for strategy in STRATEGIES:
+        spots = strategy_sweet_spots[strategy]
+        if spots:
+            sweet_spots[strategy] = {
+                "typical_diversity": sum(s["lambda"] for s in spots) / len(spots),
+                "avg_ndcg_gain": sum(s["ndcg_gain"] for s in spots) / len(spots),
+                "avg_ilad": sum(s["ilad"] for s in spots) / len(spots),
+                "avg_ilmd": sum(s["ilmd"] for s in spots) / len(spots),
+                "avg_ilad_gain": sum(s["ilad_gain"] for s in spots) / len(spots),
+                "avg_ilmd_gain": sum(s["ilmd_gain"] for s in spots) / len(spots),
+                "datasets_with_sweet_spot": float(len(spots)),
+            }
+
+    return sweet_spots
+
+
 def generate_pareto_plot(all_data: list[dict], output_path: Path, diversity_metric: str = "ilad") -> None:
     """Generate Pareto frontier plot showing relevance vs diversity tradeoff."""
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
@@ -433,70 +572,137 @@ def _log_per_strategy_diversity_tables(all_data: list[dict]) -> None:
                 )
 
 
-def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
-    """Log relevance-budgeted analysis tables."""
-    for floor in RELEVANCE_FLOORS:
-        floor_pct = int(floor * 100)
-        logger.info(f"\n=== Relevance-Budgeted Analysis (≥{floor_pct}% baseline nDCG) ===")
+def _log_per_dataset_configs(recommendations: dict, floor_pct: int) -> None:
+    """Log per-dataset config table."""
+    name_map = {
+        "ml-32m": "MovieLens-32M",
+        "lastfm": "Last.FM",
+        "amazon-video-games": "Amazon Video Games",
+        "goodreads": "Goodreads",
+    }
 
-        # Per-dataset recommendations
-        recommendations = _compute_relevance_budgeted_scores(all_data, relevance_floor=floor)
+    logger.info(f"\nBest configs per dataset (maintaining ≥{floor_pct}% baseline relevance):")
+    logger.info("| Dataset | Goal | Strategy | λ | nDCG vs Base | ILAD | ILMD |")
+    logger.info("|---------|------|----------|---|--------------|------|------|")
 
-        name_map = {
-            "ml-32m": "MovieLens-32M",
-            "lastfm": "Last.FM",
-            "amazon-video-games": "Amazon Video Games",
-            "goodreads": "Goodreads",
-        }
+    for dataset, goals in recommendations.items():
+        display_name = name_map.get(dataset, dataset)[:16]
+        for goal, config in goals.items():
+            if goal == "best_3way":
+                continue
+            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+            strategy_name = str(config["strategy"]).upper()
+            logger.info(
+                f"| {display_name:16} | {goal_display:12} | {strategy_name:8} | "
+                f"{config['lambda']:.1f} | {config['ndcg_vs_baseline']:.1%} | "
+                f"{config['ilad']:.3f} | {config['ilmd']:.3f} |"
+            )
 
-        logger.info(f"\nBest configs per dataset (maintaining ≥{floor_pct}% baseline relevance):")
-        logger.info("| Dataset | Goal | Strategy | λ | nDCG vs Base | ILAD | ILMD |")
-        logger.info("|---------|------|----------|---|--------------|------|------|")
 
-        for dataset, goals in recommendations.items():
-            display_name = name_map.get(dataset, dataset)[:16]
-            for goal, config in goals.items():
-                goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
-                strategy_name = str(config["strategy"]).upper()
-                logger.info(
-                    f"| {display_name:16} | {goal_display:12} | {strategy_name:8} | "
-                    f"{config['lambda']:.1f} | {config['ndcg_vs_baseline']:.1%} | "
-                    f"{config['ilad']:.3f} | {config['ilmd']:.3f} |"
-                )
+def _log_scorecard_table(scorecard: dict[str, dict[str, float]], floor_pct: int) -> list[tuple[str, dict]]:
+    """Log scorecard table and return sorted strategies."""
+    logger.info(f"\n=== Strategy Scorecard at {floor_pct}% floor (averaged across datasets) ===")
+    logger.info("| Strategy | Score (3-way) | nDCG Δ | ILAD (+%) | ILMD (+%) | diversity |")
+    logger.info("|----------|---------------|--------|-----------|-----------|-----------|")
+
+    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score_3way"], reverse=True)
+    for strategy, scores in sorted_strategies:
+        ndcg_delta = scores["ndcg_pct_gain"]
+        ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
+        logger.info(
+            f"| {strategy.upper():8} | {scores['combined_score_3way']:.3f}         | "
+            f"{ndcg_str:6} | "
+            f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
+            f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
+            f"{scores['typical_diversity']:.1f}       |"
+        )
+    return sorted_strategies
+
+
+def _log_per_floor_analysis(all_data: list[dict], floor: float) -> None:
+    """Log analysis for a single relevance floor."""
+    floor_pct = int(floor * 100)
+    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{floor_pct}% baseline nDCG) ===")
+
+    recommendations = _compute_relevance_budgeted_scores(all_data, relevance_floor=floor)
+    _log_per_dataset_configs(recommendations, floor_pct)
+
+    scorecard = _compute_strategy_scorecard(all_data, relevance_floor=floor)
+    sorted_strategies = _log_scorecard_table(scorecard, floor_pct)
+
+    logger.info(f"\n=== Key Findings ({floor_pct}% floor) ===")
+    if sorted_strategies:
+        best_strategy = sorted_strategies[0][0].upper()
+        best_scores = sorted_strategies[0][1]
+        logger.info(f"Best overall (3-way score): {best_strategy}")
+        logger.info(f"  nDCG: {'+' if best_scores['ndcg_pct_gain'] >= 0 else ''}{best_scores['ndcg_pct_gain']:.1f}%")
+        logger.info(f"  ILAD: +{best_scores['ilad_pct_gain']:.0f}%")
+        logger.info(f"  ILMD: +{best_scores['ilmd_pct_gain']:.0f}%")
+
+    goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
+    for goals in recommendations.values():
+        for goal, config in goals.items():
+            if goal == "best_3way":
+                continue
+            strategy_str = str(config["strategy"])
+            goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
 
-        # Strategy scorecard
-        scorecard = _compute_strategy_scorecard(all_data, relevance_floor=floor)
-        logger.info(f"\n=== Strategy Scorecard at {floor_pct}% floor (averaged across datasets) ===")
-        logger.info("| Strategy | Combined | ILAD | ILMD | nDCG Ret | Typical diversity |")
-        logger.info("|----------|----------|------|------|----------|-------------------|")
+    for goal, wins in goal_wins.items():
+        if wins:
+            winner = max(wins.items(), key=lambda x: x[1])
+            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
+            logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
 
-        sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score"], reverse=True)
-        for strategy, scores in sorted_strategies:
+
+def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
+    """Log relevance-budgeted analysis tables."""
+    # Main results: optimal point per strategy (no floor constraint)
+    logger.info("\n=== Overall Results (Optimal Operating Point) ===")
+    logger.info("Best 3-way combined score (nDCG × ILAD × ILMD) per strategy, no constraints:")
+
+    scorecard = _compute_strategy_scorecard(all_data, relevance_floor=None)
+    logger.info("| Strategy | Score | nDCG Δ | ILAD | ILMD | `diversity` |")
+    logger.info("|----------|:-----:|:------:|:----:|:----:|:-----------:|")
+
+    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score_3way"], reverse=True)
+    for strategy, scores in sorted_strategies:
+        ndcg_delta = scores["ndcg_pct_gain"]
+        ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
+        logger.info(
+            f"| **{strategy.upper()}**  | **{scores['combined_score_3way']:.3f}** | "
+            f"{ndcg_str} | "
+            f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
+            f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
+            f"{scores['typical_diversity']:.1f} |"
+        )
+
+    # Floor-constrained analysis (for detailed results)
+    for floor in RELEVANCE_FLOORS:
+        _log_per_floor_analysis(all_data, floor)
+
+    # Sweet spot analysis (no relevance loss)
+    logger.info("\n=== Sweet Spot Analysis (no relevance loss) ===")
+    sweet_spots = _compute_sweet_spots(all_data)
+
+    if sweet_spots:
+        logger.info("Best diversity gain without losing relevance:")
+        logger.info("| Strategy | diversity | nDCG Δ | ILAD (+%) | ILMD (+%) | datasets |")
+        logger.info("|----------|-----------|--------|-----------|-----------|----------|")
+
+        sorted_spots = sorted(
+            sweet_spots.items(), key=lambda x: x[1]["avg_ilad_gain"] + x[1]["avg_ilmd_gain"], reverse=True
+        )
+        for strategy, spot in sorted_spots:
+            ndcg_delta = spot["avg_ndcg_gain"]
+            ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
             logger.info(
-                f"| {strategy.upper():8} | {scores['combined_score']:.3f}    | "
-                f"{scores['ilad_at_best']:.2f} | {scores['ilmd_at_best']:.2f} | "
-                f"{scores['ndcg_retention']:.1%}    | {scores['typical_diversity']:.1f}               |"
+                f"| {strategy.upper():8} | {spot['typical_diversity']:.1f}       | "
+                f"{ndcg_str:6} | "
+                f"+{spot['avg_ilad_gain']:.0f}%      | "
+                f"+{spot['avg_ilmd_gain']:.0f}%      | "
+                f"{spot['datasets_with_sweet_spot']}/4      |"
             )
 
-        # Summary
-        logger.info(f"\n=== Key Findings ({floor_pct}% floor) ===")
-        if sorted_strategies:
-            best_strategy = sorted_strategies[0][0].upper()
-            logger.info(f"Best overall (balanced ILAD+ILMD): {best_strategy}")
-
-        # Count wins per goal
-        goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
-        for goals in recommendations.values():
-            for goal, config in goals.items():
-                strategy_str = str(config["strategy"])
-                goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
-
-        for goal, wins in goal_wins.items():
-            if wins:
-                winner = max(wins.items(), key=lambda x: x[1])
-                goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
-                logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
-
 
 def generate_report(results_dir: Path) -> None:
     """Generate plots from JSON results. Main findings are in benchmarks/README.md."""
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
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From 6ca13f8bdb3652f0cfa39afac11eb7fb8fce4046 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 10:53:36 +0100
Subject: [PATCH 47/69] Updated results

---
 benchmarks/README.md           |  76 +++++-----
 benchmarks/core/report.py      | 244 +++++++++++++++++----------------
 benchmarks/results/latency.png | Bin 80442 -> 80997 bytes
 3 files changed, 166 insertions(+), 154 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 3c0aa0f..01274ee 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -25,63 +25,73 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We measure two diversity metrics: **ILAD** (average pairwise diversity—higher = more variety) and **ILMD** (minimum pairwise diversity—higher = fewer similar pairs).
+We answer two questions:
 
-### Key Insight: Diversification Can *Improve* Relevance
+1. **Accuracy**: Which strategy achieves the best relevance (nDCG)?
+2. **Diversity under constraint**: How much diversity can we gain while maintaining relevance?
 
-A surprising finding: moderate diversification often **improves** relevance (nDCG), not just diversity. This happens because diversifiers act as intelligent tie-breakers, helping surface items from different preference modes.
+We measure two diversity metrics:
+- **ILAD** (Intra-List Average Distance): average pairwise diversity—higher means more variety
+- **ILMD** (Intra-List Minimum Distance): minimum pairwise diversity—higher means fewer similar pairs
 
-### Overall Results
+**Why can diversification improve nDCG?** In offline leave-one-out evaluation, the "baseline" ranking (no diversification) often places many near-duplicate items at the top. Diversifiers act as intelligent tie-breakers: by spreading selections across different item neighborhoods, they increase the chance of including the held-out test item. This is why moderate diversification can *improve* relevance, not just diversity.
 
-Each strategy's **sweet spot**—the best operating point that maintains or improves relevance:
+### Table 1: Accuracy Leaderboard
 
-| Strategy | `diversity` | nDCG Δ | ILAD (+%) | ILMD (+%) |
-|----------|:-----------:|:------:|:---------:|:---------:|
-| **DPP**  | 0.8 | **+3.9%** | +47% | **+101%** |
-| SSD      | 0.8 | +3.1% | +34% | +60% |
-| MMR      | 0.7 | +2.1% | +20% | +51% |
-| MSD      | 0.3 | +1.2% | +33% | +18% |
+Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
 
-*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Sweet spot = highest diversity while improving or maintaining relevance. Results averaged across 4 datasets, 10 runs.*
+| Strategy | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
+|----------|:------:|:---------:|:---------:|:-----------:|
+| **DPP**  | **+5.1%** | +31% | +73% | 0.6 |
+| SSD      | +4.7% | +19% | +34% | 0.7 |
+| MMR      | +3.2% | +17% | +34% | 0.6 |
+| MSD      | +2.2% | +21% | +10% | 0.2 |
 
-**DPP leads overall**, achieving +3.9% nDCG improvement while also boosting ILAD by 47% and ILMD by 101%—a true "free lunch."
+*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results averaged across 4 datasets, 10 runs.*
 
-<details>
-<summary>Results at Specific Relevance Floors</summary>
+**DPP leads**, achieving +5.1% nDCG improvement while also boosting ILAD by 31% and ILMD by 73%.
+
+### Table 2: Diversity Leaderboard (≥99% Relevance)
+
+Best diversity per strategy while maintaining ≥99% of baseline nDCG, ranked by diversity score:
 
-These tables show results when you allow some relevance loss (vs the sweet spot above which requires no loss):
+| Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
+|----------|:---------------:|:------:|:---------:|:---------:|:-----------:|
+| **DPP**  | **0.968** | +3.7% | +51% | **+107%** | 0.8 |
+| SSD      | 0.641 | +3.1% | +34% | +60% | 0.8 |
+| MMR      | 0.578 | +1.8% | +26% | +61% | 0.7 |
+| MSD      | 0.385 | +0.8% | +39% | +20% | 0.3 |
 
-#### 99% Relevance Floor (≤1% loss allowed)
+*Diversity Score = geometric mean of normalized ILAD/ILMD gains (using feasible-max normalization). Higher = better balanced diversity.*
 
-| Strategy | `diversity` | nDCG Δ | ILAD | ILMD |
-|----------|:-----------:|:------:|:----:|:----:|
-| **DPP**  | 0.8 | **+3.7%** | 0.60 (+51%) | **0.25 (+107%)** |
-| SSD      | 0.8 | +3.1% | 0.55 (+34%) | 0.21 (+60%) |
-| MMR      | 0.7 | +1.8% | 0.53 (+26%) | 0.22 (+61%) |
-| MSD      | 0.3 | +0.8% | 0.57 (+39%) | 0.16 (+20%) |
+### Table 3: Diversity Leaderboard (≥95% Relevance)
 
-#### 95% Relevance Floor (≤5% loss allowed)
+Best diversity per strategy while maintaining ≥95% of baseline nDCG:
 
-| Strategy | `diversity` | nDCG Δ | ILAD | ILMD |
-|----------|:-----------:|:------:|:----:|:----:|
-| **DPP**  | 0.8 | **+2.8%** | 0.63 (+58%) | **0.26 (+117%)** |
-| MMR      | 0.8 | -2.4% | 0.61 (+53%) | 0.27 (+122%) |
-| SSD      | 0.9 | +0.4% | 0.60 (+52%) | 0.23 (+88%) |
-| MSD      | 0.5 | -1.6% | 0.63 (+57%) | 0.18 (+32%) |
+| Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
+|----------|:---------------:|:------:|:---------:|:---------:|:-----------:|
+| **DPP**  | **0.884** | +2.8% | +58% | **+117%** | 0.8 |
+| MMR      | 0.879 | -2.4% | +53% | +122% | 0.8 |
+| SSD      | 0.704 | +0.4% | +52% | +88% | 0.9 |
+| MSD      | 0.477 | -1.6% | +57% | +32% | 0.5 |
 
 *With a looser floor, you can push diversity higher—but DPP still leads with positive nDCG gains.*
 
-</details>
+### Summary
+
+- **DPP wins on all three leaderboards**: best accuracy, best diversity under both 99% and 95% floors
+- **At strict 99% floor**: DPP achieves +51% ILAD and +107% ILMD while still *improving* nDCG (+3.7%)
+- **MSD** excels at pushing ILAD very high, but at the cost of ILMD and sometimes relevance
 
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |
 |------|----------|:-----------:|-------|
-| **Best overall** | **DPP** | 0.7-0.8 | Best combined score; improves relevance AND diversity |
+| **Best overall** | **DPP** | 0.6-0.8 | Best on all leaderboards; improves relevance AND diversity |
 | **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD, but worse ILMD and may lose relevance |
 | **Minimize similar pairs (ILMD)** | **DPP** | 0.7-0.8 | Best ILMD with strong ILAD too |
 | **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
-| **Simple implementation** | **MMR** | 0.7-0.8 | Easiest to implement, good results |
+| **Simple implementation** | **MMR** | 0.6-0.7 | Easiest to implement, good results |
 
 *`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
 
diff --git a/benchmarks/core/report.py b/benchmarks/core/report.py
index f0ff876..b41e0af 100644
--- a/benchmarks/core/report.py
+++ b/benchmarks/core/report.py
@@ -189,22 +189,13 @@ def _compute_combined_score_2way(
     point: dict, ilad_baseline: float, ilad_range: float, ilmd_baseline: float, ilmd_range: float
 ) -> float:
     """Compute 2-way combined score (ILAD × ILMD)."""
+    if ilad_range <= 0 or ilmd_range <= 0:
+        return 0.0
     ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
     ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
     return (ilad_gain * ilmd_gain) ** 0.5
 
 
-def _compute_combined_score_3way(
-    point: dict, baseline_ndcg: float, ilad_baseline: float, ilad_range: float, ilmd_baseline: float, ilmd_range: float
-) -> float:
-    """Compute 3-way combined score (nDCG × ILAD × ILMD)."""
-    ilad_gain = max(0.0, min(1.0, (point["ilad"] - ilad_baseline) / ilad_range))
-    ilmd_gain = max(0.0, min(1.0, (point["ilmd"] - ilmd_baseline) / ilmd_range))
-    ndcg_ratio = point["ndcg"] / baseline_ndcg if baseline_ndcg > 0 else 1.0
-    ndcg_gain = max(0.0, min(1.0, (ndcg_ratio - 0.5) / 1.0))
-    return (ilad_gain * ilmd_gain * ndcg_gain) ** (1 / 3)
-
-
 def _update_accumulator(
     acc: dict[str, list[float]],
     point: dict,
@@ -216,35 +207,69 @@ def _update_accumulator(
 ) -> None:
     """Update accumulator with a point's metrics."""
     acc["scores"].append(_compute_combined_score_2way(point, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range))
-    acc["scores_3way"].append(
-        _compute_combined_score_3way(point, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range)
-    )
     acc["ilads"].append(point["ilad"])
     acc["ilmds"].append(point["ilmd"])
-    acc["ndcg_ret"].append(point["ndcg"] / baseline_ndcg)
+    acc["ndcg_ret"].append(point["ndcg"] / baseline_ndcg if baseline_ndcg > 0 else 0)
     acc["lambdas"].append(point["lambda"])
     if ilad_baseline > 0:
         acc["ilad_pct"].append((point["ilad"] - ilad_baseline) / ilad_baseline * 100)
     if ilmd_baseline > 0:
         acc["ilmd_pct"].append((point["ilmd"] - ilmd_baseline) / ilmd_baseline * 100)
-    acc["ndcg_gain"].append((point["ndcg"] / baseline_ndcg - 1) * 100)
+    acc["ndcg_gain"].append((point["ndcg"] / baseline_ndcg - 1) * 100 if baseline_ndcg > 0 else 0)
+
+
+def _get_bounds_for_dataset(
+    dataset_points: list[dict], feasible_all: list[dict], relevance_floor: float | None, rank_by_ndcg: bool
+) -> tuple[float, float, float, float] | None:
+    """Get normalization bounds for a dataset (supports feasible-max normalization)."""
+    bounds = _get_dataset_baseline_and_bounds(dataset_points)
+    if bounds is None:
+        return None
+
+    baseline_ndcg, ilad_baseline, ilad_max_global, ilmd_baseline, ilmd_max_global = bounds
+
+    # For diversity ranking with floor, use feasible-max normalization
+    if relevance_floor is not None and not rank_by_ndcg and feasible_all:
+        ilad_max = max(p["ilad"] for p in feasible_all)
+        ilmd_max = max(p["ilmd"] for p in feasible_all)
+    else:
+        ilad_max = ilad_max_global
+        ilmd_max = ilmd_max_global
+
+    return ilad_baseline, ilad_max - ilad_baseline, ilmd_baseline, ilmd_max - ilmd_baseline
+
+
+def _select_best_point(
+    feasible: list[dict],
+    rank_by_ndcg: bool,
+    ilad_baseline: float,
+    ilad_range: float,
+    ilmd_baseline: float,
+    ilmd_range: float,
+) -> dict:
+    """Select the best point based on ranking mode."""
+    if rank_by_ndcg:
+        return max(feasible, key=lambda p: (p["ndcg"], -p["lambda"]))
+    else:
+        return max(
+            feasible,
+            key=lambda p: (
+                _compute_combined_score_2way(p, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range),
+                p["ndcg"],
+                -p["lambda"],
+            ),
+        )
 
 
 def _compute_strategy_scorecard(
-    all_data: list[dict], relevance_floor: float | None = None
+    all_data: list[dict], relevance_floor: float | None = None, rank_by_ndcg: bool = False
 ) -> dict[str, dict[str, float]]:
-    """
-    Compute per-strategy aggregate scores across all datasets.
-
-    If relevance_floor is None, finds the optimal point for each strategy (no constraint).
-    If relevance_floor is set, only considers points above that fraction of baseline nDCG.
-    """
+    """Compute per-strategy aggregate scores across all datasets."""
     datasets = sorted(set(p["dataset"] for p in all_data))
 
     accumulators: dict[str, dict[str, list[float]]] = {
         s: {
             "scores": [],
-            "scores_3way": [],
             "ilads": [],
             "ilmds": [],
             "ilad_pct": [],
@@ -262,31 +287,28 @@ def _compute_strategy_scorecard(
         if bounds is None:
             continue
 
-        baseline_ndcg, ilad_baseline, ilad_max, ilmd_baseline, ilmd_max = bounds
-        ilad_range = ilad_max - ilad_baseline
-        ilmd_range = ilmd_max - ilmd_baseline
+        baseline_ndcg = bounds[0]
+        ndcg_floor = relevance_floor * baseline_ndcg if relevance_floor else 0
+        feasible_all = [p for p in dataset_points if p["ndcg"] >= ndcg_floor] if relevance_floor else dataset_points
+
+        if not feasible_all:
+            continue
+
+        norm_bounds = _get_bounds_for_dataset(dataset_points, feasible_all, relevance_floor, rank_by_ndcg)
+        if norm_bounds is None:
+            continue
+
+        ilad_baseline, ilad_range, ilmd_baseline, ilmd_range = norm_bounds
 
         for strategy in STRATEGIES:
             strategy_points = [p for p in dataset_points if p["strategy"] == strategy]
-
-            if relevance_floor is not None:
-                ndcg_floor = relevance_floor * baseline_ndcg
-                feasible = [p for p in strategy_points if p["ndcg"] >= ndcg_floor]
-            else:
-                feasible = strategy_points
+            feasible = [p for p in strategy_points if p["ndcg"] >= ndcg_floor] if relevance_floor else strategy_points
 
             if not feasible:
                 continue
 
-            best_point = max(
-                feasible,
-                key=lambda p: (
-                    _compute_combined_score_3way(
-                        p, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range
-                    ),
-                    p["ndcg"],
-                    -p["lambda"],
-                ),
+            best_point = _select_best_point(
+                feasible, rank_by_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range
             )
             _update_accumulator(
                 accumulators[strategy], best_point, baseline_ndcg, ilad_baseline, ilad_range, ilmd_baseline, ilmd_range
@@ -296,8 +318,7 @@ def _compute_strategy_scorecard(
     for strategy, acc in accumulators.items():
         if acc["scores"]:
             scorecard[strategy] = {
-                "combined_score": sum(acc["scores"]) / len(acc["scores"]),
-                "combined_score_3way": sum(acc["scores_3way"]) / len(acc["scores_3way"]),
+                "diversity_score": sum(acc["scores"]) / len(acc["scores"]),
                 "ilad_at_best": sum(acc["ilads"]) / len(acc["ilads"]),
                 "ilmd_at_best": sum(acc["ilmds"]) / len(acc["ilmds"]),
                 "ilad_pct_gain": sum(acc["ilad_pct"]) / len(acc["ilad_pct"]) if acc["ilad_pct"] else 0.0,
@@ -599,109 +620,90 @@ def _log_per_dataset_configs(recommendations: dict, floor_pct: int) -> None:
             )
 
 
-def _log_scorecard_table(scorecard: dict[str, dict[str, float]], floor_pct: int) -> list[tuple[str, dict]]:
+def _log_scorecard_table(
+    scorecard: dict[str, dict[str, float]], title: str, rank_by_ndcg: bool = False
+) -> list[tuple[str, dict]]:
     """Log scorecard table and return sorted strategies."""
-    logger.info(f"\n=== Strategy Scorecard at {floor_pct}% floor (averaged across datasets) ===")
-    logger.info("| Strategy | Score (3-way) | nDCG Δ | ILAD (+%) | ILMD (+%) | diversity |")
-    logger.info("|----------|---------------|--------|-----------|-----------|-----------|")
+    logger.info(f"\n=== {title} ===")
+
+    if rank_by_ndcg:
+        logger.info("| Strategy | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |")
+        logger.info("|----------|:------:|:---------:|:---------:|:-----------:|")
+        sort_key = lambda x: x[1]["ndcg_pct_gain"]
+    else:
+        logger.info("| Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |")
+        logger.info("|----------|:---------------:|:------:|:---------:|:---------:|:-----------:|")
+        sort_key = lambda x: x[1]["diversity_score"]
+
+    sorted_strategies = sorted(scorecard.items(), key=sort_key, reverse=True)
 
-    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score_3way"], reverse=True)
     for strategy, scores in sorted_strategies:
         ndcg_delta = scores["ndcg_pct_gain"]
         ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
-        logger.info(
-            f"| {strategy.upper():8} | {scores['combined_score_3way']:.3f}         | "
-            f"{ndcg_str:6} | "
-            f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
-            f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
-            f"{scores['typical_diversity']:.1f}       |"
-        )
+
+        if rank_by_ndcg:
+            logger.info(
+                f"| **{strategy.upper()}** | {ndcg_str} | "
+                f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
+                f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
+                f"{scores['typical_diversity']:.1f} |"
+            )
+        else:
+            logger.info(
+                f"| **{strategy.upper()}** | {scores['diversity_score']:.3f} | "
+                f"{ndcg_str} | "
+                f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
+                f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
+                f"{scores['typical_diversity']:.1f} |"
+            )
+
     return sorted_strategies
 
 
 def _log_per_floor_analysis(all_data: list[dict], floor: float) -> None:
-    """Log analysis for a single relevance floor."""
+    """Log analysis for a single relevance floor (diversity leaderboard)."""
     floor_pct = int(floor * 100)
-    logger.info(f"\n=== Relevance-Budgeted Analysis (≥{floor_pct}% baseline nDCG) ===")
-
-    recommendations = _compute_relevance_budgeted_scores(all_data, relevance_floor=floor)
-    _log_per_dataset_configs(recommendations, floor_pct)
 
-    scorecard = _compute_strategy_scorecard(all_data, relevance_floor=floor)
-    sorted_strategies = _log_scorecard_table(scorecard, floor_pct)
+    # Diversity leaderboard: maximize diversity under floor constraint
+    scorecard = _compute_strategy_scorecard(all_data, relevance_floor=floor, rank_by_ndcg=False)
+    title = f"Diversity Leaderboard (≥{floor_pct}% baseline nDCG)"
+    sorted_strategies = _log_scorecard_table(scorecard, title, rank_by_ndcg=False)
 
-    logger.info(f"\n=== Key Findings ({floor_pct}% floor) ===")
     if sorted_strategies:
         best_strategy = sorted_strategies[0][0].upper()
         best_scores = sorted_strategies[0][1]
-        logger.info(f"Best overall (3-way score): {best_strategy}")
+        logger.info(f"\nBest diversity under {floor_pct}% floor: {best_strategy}")
+        logger.info(f"  Diversity score: {best_scores['diversity_score']:.3f}")
         logger.info(f"  nDCG: {'+' if best_scores['ndcg_pct_gain'] >= 0 else ''}{best_scores['ndcg_pct_gain']:.1f}%")
         logger.info(f"  ILAD: +{best_scores['ilad_pct_gain']:.0f}%")
         logger.info(f"  ILMD: +{best_scores['ilmd_pct_gain']:.0f}%")
 
-    goal_wins: dict[str, dict[str, int]] = {"max_ilad": {}, "max_ilmd": {}, "best_combined": {}}
-    for goals in recommendations.values():
-        for goal, config in goals.items():
-            if goal == "best_3way":
-                continue
-            strategy_str = str(config["strategy"])
-            goal_wins[goal][strategy_str] = goal_wins[goal].get(strategy_str, 0) + 1
-
-    for goal, wins in goal_wins.items():
-        if wins:
-            winner = max(wins.items(), key=lambda x: x[1])
-            goal_display = {"max_ilad": "Max ILAD", "max_ilmd": "Max ILMD", "best_combined": "Best Overall"}[goal]
-            logger.info(f"  {goal_display}: {winner[0].upper()} ({winner[1]}/{len(recommendations)} datasets)")
-
 
 def _log_relevance_budgeted_analysis(all_data: list[dict]) -> None:
-    """Log relevance-budgeted analysis tables."""
-    # Main results: optimal point per strategy (no floor constraint)
-    logger.info("\n=== Overall Results (Optimal Operating Point) ===")
-    logger.info("Best 3-way combined score (nDCG × ILAD × ILMD) per strategy, no constraints:")
-
-    scorecard = _compute_strategy_scorecard(all_data, relevance_floor=None)
-    logger.info("| Strategy | Score | nDCG Δ | ILAD | ILMD | `diversity` |")
-    logger.info("|----------|:-----:|:------:|:----:|:----:|:-----------:|")
+    """Log the three main tables: accuracy leaderboard + two diversity leaderboards."""
+    # Table 1: Accuracy leaderboard (best nDCG, no constraints)
+    logger.info("\n" + "=" * 60)
+    logger.info("TABLE 1: ACCURACY LEADERBOARD")
+    logger.info("Best nDCG per strategy (selecting λ that maximizes nDCG)")
+    logger.info("=" * 60)
 
-    sorted_strategies = sorted(scorecard.items(), key=lambda x: x[1]["combined_score_3way"], reverse=True)
-    for strategy, scores in sorted_strategies:
-        ndcg_delta = scores["ndcg_pct_gain"]
-        ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
-        logger.info(
-            f"| **{strategy.upper()}**  | **{scores['combined_score_3way']:.3f}** | "
-            f"{ndcg_str} | "
-            f"{scores['ilad_at_best']:.2f} (+{scores['ilad_pct_gain']:.0f}%) | "
-            f"{scores['ilmd_at_best']:.2f} (+{scores['ilmd_pct_gain']:.0f}%) | "
-            f"{scores['typical_diversity']:.1f} |"
-        )
+    accuracy_scorecard = _compute_strategy_scorecard(all_data, relevance_floor=None, rank_by_ndcg=True)
+    _log_scorecard_table(accuracy_scorecard, "Best Relevance (Accuracy)", rank_by_ndcg=True)
 
-    # Floor-constrained analysis (for detailed results)
+    # Table 2 & 3: Diversity leaderboards under floor constraints
     for floor in RELEVANCE_FLOORS:
-        _log_per_floor_analysis(all_data, floor)
-
-    # Sweet spot analysis (no relevance loss)
-    logger.info("\n=== Sweet Spot Analysis (no relevance loss) ===")
-    sweet_spots = _compute_sweet_spots(all_data)
+        floor_pct = int(floor * 100)
+        logger.info("\n" + "=" * 60)
+        logger.info(f"TABLE {RELEVANCE_FLOORS.index(floor) + 2}: DIVERSITY LEADERBOARD (≥{floor_pct}% nDCG)")
+        logger.info(f"Best diversity per strategy under ≥{floor_pct}% baseline relevance")
+        logger.info("Ranked by geometric mean of normalized ILAD/ILMD gains")
+        logger.info("Uses feasible-max normalization for fair comparison")
+        logger.info("=" * 60)
 
-    if sweet_spots:
-        logger.info("Best diversity gain without losing relevance:")
-        logger.info("| Strategy | diversity | nDCG Δ | ILAD (+%) | ILMD (+%) | datasets |")
-        logger.info("|----------|-----------|--------|-----------|-----------|----------|")
+        _log_per_floor_analysis(all_data, floor)
 
-        sorted_spots = sorted(
-            sweet_spots.items(), key=lambda x: x[1]["avg_ilad_gain"] + x[1]["avg_ilmd_gain"], reverse=True
-        )
-        for strategy, spot in sorted_spots:
-            ndcg_delta = spot["avg_ndcg_gain"]
-            ndcg_str = f"+{ndcg_delta:.1f}%" if ndcg_delta >= 0 else f"{ndcg_delta:.1f}%"
-            logger.info(
-                f"| {strategy.upper():8} | {spot['typical_diversity']:.1f}       | "
-                f"{ndcg_str:6} | "
-                f"+{spot['avg_ilad_gain']:.0f}%      | "
-                f"+{spot['avg_ilmd_gain']:.0f}%      | "
-                f"{spot['datasets_with_sweet_spot']}/4      |"
-            )
+    # Per-strategy diversity sweep tables (for detailed results)
+    _log_per_strategy_diversity_tables(all_data)
 
 
 def generate_report(results_dir: Path) -> None:
diff --git a/benchmarks/results/latency.png b/benchmarks/results/latency.png
index 3f10a087e80e6f82857e411fc318cb4a62c455fb..2c007df9eda2c2ab8ef5ffd0cbe95036b10f81c3 100644
GIT binary patch
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Dl_Y)v


From a85c0441a48921d85aa84c343b98f051c9c758eb Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 14:50:12 +0100
Subject: [PATCH 48/69] Changed split logic, updated results

---
 benchmarks/README.md                       |   45 +-
 benchmarks/core/embeddings.py              |   22 +-
 benchmarks/core/runner.py                  |  103 +-
 benchmarks/results/amazon-video-games.json | 4226 ++++++++++----------
 benchmarks/results/goodreads.json          | 4226 ++++++++++----------
 benchmarks/results/lastfm.json             | 4226 ++++++++++----------
 benchmarks/results/latency.png             |  Bin 80997 -> 81737 bytes
 benchmarks/results/ml-32m.json             | 4218 +++++++++----------
 benchmarks/results/pareto_ilad.png         |  Bin 316527 -> 316015 bytes
 benchmarks/results/pareto_ilmd.png         |  Bin 330944 -> 335201 bytes
 10 files changed, 8566 insertions(+), 8500 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 01274ee..97d26b8 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -2,10 +2,10 @@
 
 This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**, and **SSD** across 4 recommendation datasets.
 
-For each dataset, we use a leave-one-out evaluation protocol (meaning we hold out one item
-per user as the test set), generate candidate items based on similar items, then rerank with each
-diversification strategy. We measure relevance (nDCG) and diversity (ILAD, ILMD) to trace the
-Pareto frontier of the relevance-diversity tradeoff.
+For each dataset, we use a leave-one-out evaluation protocol: we hold out one item per user as the
+test set, train embeddings on the remaining interactions (preventing leakage), generate candidate items,
+then rerank with each diversification strategy. We measure relevance (nDCG) and diversity (ILAD, ILMD)
+to answer: *which strategy gives the best relevance-diversity tradeoff?*
 
 In addition, we measure latency of each strategy as the number of candidates scales.
 
@@ -42,14 +42,14 @@ Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
 
 | Strategy | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
 |----------|:------:|:---------:|:---------:|:-----------:|
-| **DPP**  | **+5.1%** | +31% | +73% | 0.6 |
-| SSD      | +4.7% | +19% | +34% | 0.7 |
-| MMR      | +3.2% | +17% | +34% | 0.6 |
-| MSD      | +2.2% | +21% | +10% | 0.2 |
+| **DPP**  | **+3.1%** | +26% | +54% | 0.5 |
+| SSD      | +2.8% | +16% | +25% | 0.6 |
+| MMR      | +1.5% | +11% | +17% | 0.4 |
+| MSD      | +1.0% | +17% | +5% | 0.2 |
 
 *nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results averaged across 4 datasets, 10 runs.*
 
-**DPP leads**, achieving +5.1% nDCG improvement while also boosting ILAD by 31% and ILMD by 73%.
+**DPP leads**, achieving +3.1% nDCG improvement while also boosting ILAD by 26% and ILMD by 54%.
 
 ### Table 2: Diversity Leaderboard (≥99% Relevance)
 
@@ -57,10 +57,10 @@ Best diversity per strategy while maintaining ≥99% of baseline nDCG, ranked by
 
 | Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
 |----------|:---------------:|:------:|:---------:|:---------:|:-----------:|
-| **DPP**  | **0.968** | +3.7% | +51% | **+107%** | 0.8 |
-| SSD      | 0.641 | +3.1% | +34% | +60% | 0.8 |
-| MMR      | 0.578 | +1.8% | +26% | +61% | 0.7 |
-| MSD      | 0.385 | +0.8% | +39% | +20% | 0.3 |
+| **DPP**  | **0.937** | +1.8% | +44% | **+86%** | 0.7 |
+| SSD      | 0.625 | +2.0% | +29% | +43% | 0.8 |
+| MMR      | 0.570 | +0.7% | +22% | +42% | 0.7 |
+| MSD      | 0.267 | +0.5% | +33% | +9% | 0.3 |
 
 *Diversity Score = geometric mean of normalized ILAD/ILMD gains (using feasible-max normalization). Higher = better balanced diversity.*
 
@@ -70,17 +70,17 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG:
 
 | Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
 |----------|:---------------:|:------:|:---------:|:---------:|:-----------:|
-| **DPP**  | **0.884** | +2.8% | +58% | **+117%** | 0.8 |
-| MMR      | 0.879 | -2.4% | +53% | +122% | 0.8 |
-| SSD      | 0.704 | +0.4% | +52% | +88% | 0.9 |
-| MSD      | 0.477 | -1.6% | +57% | +32% | 0.5 |
+| **DPP**  | **0.889** | +0.2% | +49% | **+99%** | 0.8 |
+| MMR      | 0.779 | -2.9% | +39% | +91% | 0.8 |
+| SSD      | 0.537 | +1.5% | +31% | +49% | 0.8 |
+| MSD      | 0.485 | -2.3% | +54% | +24% | 0.4 |
 
 *With a looser floor, you can push diversity higher—but DPP still leads with positive nDCG gains.*
 
 ### Summary
 
 - **DPP wins on all three leaderboards**: best accuracy, best diversity under both 99% and 95% floors
-- **At strict 99% floor**: DPP achieves +51% ILAD and +107% ILMD while still *improving* nDCG (+3.7%)
+- **At strict 99% floor**: DPP achieves +44% ILAD and +86% ILMD while still *improving* nDCG (+1.8%)
 - **MSD** excels at pushing ILAD very high, but at the cost of ILMD and sometimes relevance
 
 ### Recommendations
@@ -269,11 +269,18 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
 - **Runs**: 10 runs per dataset with different random seeds for robust results (20,000 total evaluations)
-- **Embeddings**: 64-dim SVD on item co-occurrence matrix
+- **Embeddings**: 64-dim SVD on item co-occurrence matrix, **trained on training data only** (held-out items excluded to prevent leakage)
 - **Candidates**: Top-100 similar items per profile item
 - **Selection**: k=10 items selected by each strategy
 - **`diversity` sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
+> **Evaluation setting**: We evaluate diversification as a reranking step in a transductive next-item
+> prediction setting. Item embeddings and candidate generation are learned from the interaction graph
+> with the specific held-out edges removed; rerankers are compared on the resulting candidate lists.
+> The held-out item is eligible as a candidate (we're trying to retrieve it), but other already-interacted
+> items are excluded. This setup isolates the diversification question: "given fixed candidates and scores,
+> which reranking strategy produces the best relevance-diversity tradeoff?"
+
 ### Relevance-Budgeted Evaluation
 
 We use a **relevance floor** approach to ensure fair comparison:
diff --git a/benchmarks/core/embeddings.py b/benchmarks/core/embeddings.py
index 27419c9..8fbe5b2 100644
--- a/benchmarks/core/embeddings.py
+++ b/benchmarks/core/embeddings.py
@@ -59,9 +59,25 @@ def get_candidates(
     similarity_matrix: sparse.csr_matrix,
     topk_per_item: int = 50,
     max_candidates: int = 1000,
+    exclude_items: set[int] | None = None,
 ) -> tuple[NDArray[np.int64], NDArray[np.float32]]:
-    """Generate candidate items for a user based on their profile."""
-    profile_set = set(profile_items)
+    """
+    Generate candidate items for a user based on their profile.
+
+    Args:
+    ----
+        profile_items: Items in user's profile (used to generate candidates)
+        similarity_matrix: Item-item similarity matrix
+        topk_per_item: Top-k similar items per profile item
+        max_candidates: Maximum number of candidates to return
+        exclude_items: Items to exclude from candidates (e.g., already-seen items)
+
+    """
+    # Exclude profile items and any additional exclusions
+    exclude_set = set(profile_items)
+    if exclude_items is not None:
+        exclude_set |= exclude_items
+
     scores: dict[int, float] = {}
 
     for item in profile_items:
@@ -73,7 +89,7 @@ def get_candidates(
             indices, similarities = indices[top_idx], similarities[top_idx]
 
         for idx, sim in zip(indices, similarities):
-            if idx not in profile_set:
+            if idx not in exclude_set:
                 scores[idx] = scores.get(idx, 0.0) + sim
 
     if not scores:
diff --git a/benchmarks/core/runner.py b/benchmarks/core/runner.py
index fd29131..e792796 100644
--- a/benchmarks/core/runner.py
+++ b/benchmarks/core/runner.py
@@ -38,17 +38,10 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
             existing_runs = {int(k): v for k, v in existing["per_run_results"].items()}
             logger.info(f"Resuming: found {len(existing_runs)} existing runs")
 
-    # Load and prepare data (once, shared across runs)
-    logger.debug("[1/4] Loading dataset...")
+    # Load raw data (once, shared across runs)
+    logger.info("[1/4] Loading dataset...")
     data = load_dataset(config.dataset, config.min_interactions, config.rating_threshold)
 
-    logger.debug("[2/4] Generating embeddings...")
-    embeddings = generate_embeddings(data, dim=config.embedding_dim, seed=config.seed)
-
-    logger.debug("[3/4] Computing similarity matrix...")
-    similarity = compute_similarity_matrix(embeddings, top_k=100)
-    logger.debug(f"Similarity matrix: {similarity.shape}, nnz={similarity.nnz:,}")
-
     # Run multiple times with different seeds for robustness
     per_run_results: dict[int, list[dict]] = existing_runs.copy()
 
@@ -61,18 +54,40 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
         run_seed = config.seed + run_idx
         rng = np.random.default_rng(run_seed)
 
-        # Sample users (different sample per run)
+        # Sample users and their held-out items FIRST (before training)
         user_counts = np.bincount(data.user_ids, minlength=data.n_users)
         eligible = np.where(user_counts >= 2)[0]
-        sampled = rng.choice(eligible, min(len(eligible), config.sample_users), replace=False)
+        sampled_users = rng.choice(eligible, min(len(eligible), config.sample_users), replace=False)
+
+        # For each sampled user, select their held-out item
+        holdout_map: dict[int, int] = {}  # user_id -> held-out item_id
+        for user_id in sampled_users:
+            user_mask = data.user_ids == user_id
+            user_items = data.item_ids[user_mask]
+            holdout_idx = rng.integers(len(user_items))
+            holdout_map[user_id] = user_items[holdout_idx]
+
+        # Create training data by removing held-out edges (leakage-free)
+        logger.info(
+            f"[2/4] Run {run_idx + 1}/{config.n_runs}: Creating training data (removing {len(holdout_map)} held-out edges)..."
+        )
+        train_data = _create_training_data(data, holdout_map)
+
+        # Train embeddings on training data only (no leakage)
+        logger.info(f"[2/4] Run {run_idx + 1}/{config.n_runs}: Generating embeddings...")
+        embeddings = generate_embeddings(train_data, dim=config.embedding_dim, seed=run_seed)
+
+        logger.info(f"[3/4] Run {run_idx + 1}/{config.n_runs}: Computing similarity matrix...")
+        similarity = compute_similarity_matrix(embeddings, top_k=100)
 
         run_desc = f"Run {run_idx + 1}/{config.n_runs}" if config.n_runs > 1 else "Users"
-        logger.debug(f"[4/4] {run_desc}: Evaluating {len(sampled)} users...")
+        logger.info(f"[4/4] {run_desc}: Evaluating {len(sampled_users)} users...")
 
-        # Run evaluation for this run
+        # Run evaluation for this run (use train_data for profile to avoid leakage)
         run_results = []
-        for user_id in tqdm(sampled, desc=run_desc):
-            user_results = _evaluate_user(user_id, data, embeddings, similarity, config, rng)
+        for user_id in tqdm(sampled_users, desc=run_desc):
+            test_item = holdout_map[user_id]
+            user_results = _evaluate_user(user_id, test_item, train_data, data, embeddings, similarity, config)
             run_results.extend(user_results)
 
         # Save this run immediately (for resumability)
@@ -104,7 +119,7 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
     # Save final results
     with open(output_path, "w") as f:
         json.dump(output, f, indent=2)
-    logger.debug(f"Saved results to: {output_path}")
+    logger.info(f"Saved results to: {output_path}")
 
     # Print summary table (keep as print for nice formatting)
     _print_summary(aggregated)
@@ -112,6 +127,26 @@ def run_benchmark(config: BenchmarkConfig) -> dict:
     return output
 
 
+def _create_training_data(data: InteractionData, holdout_map: dict[int, int]) -> InteractionData:
+    """Create training data by removing held-out edges (vectorized for speed)."""
+    # Build lookup array: holdout_item[user] = item to hold out, or -1 if not in holdout
+    holdout_item = np.full(data.n_users, -1, dtype=data.item_ids.dtype)
+    for user_id, item_id in holdout_map.items():
+        holdout_item[user_id] = item_id
+
+    # Vectorized mask: remove exactly the (user, holdout_item[user]) edge for users in holdout_map
+    is_holdout_user = holdout_item[data.user_ids] != -1
+    is_holdout_edge = is_holdout_user & (data.item_ids == holdout_item[data.user_ids])
+    keep_mask = ~is_holdout_edge
+
+    return InteractionData(
+        user_ids=data.user_ids[keep_mask],
+        item_ids=data.item_ids[keep_mask],
+        n_users=data.n_users,
+        n_items=data.n_items,
+    )
+
+
 def _save_intermediate(
     output_path: Path, dataset_name: str, data: InteractionData, config: BenchmarkConfig, per_run_results: dict
 ) -> None:
@@ -139,28 +174,36 @@ def _save_intermediate(
 
 def _evaluate_user(
     user_id: int,
-    data: InteractionData,
+    test_item: int,
+    train_data: InteractionData,
+    original_data: InteractionData,
     embeddings: np.ndarray,
     similarity: sparse.csr_matrix,
     config: BenchmarkConfig,
-    rng: np.random.Generator,
 ) -> list[dict]:
-    """Evaluate all strategies for a single user using leave-one-out."""
-    # Get user's items
-    mask = data.user_ids == user_id
-    profile = data.item_ids[mask]
-
-    if len(profile) < 2:
+    """
+    Evaluate all strategies for a single user using leave-one-out.
+
+    Uses next-item prediction: test_item is eligible as a candidate,
+    but other already-interacted items are excluded.
+    """
+    # Get user's profile from training data (held-out item already removed)
+    mask = train_data.user_ids == user_id
+    profile_items = train_data.item_ids[mask]
+    test_items = np.array([test_item])
+
+    if len(profile_items) < 1:
         return []
 
-    # Leave-one-out split
-    test_idx = rng.integers(len(profile))
-    test_items = np.array([profile[test_idx]])
-    profile_items = np.delete(profile, test_idx)
+    # Get all items user has interacted with (from original data)
+    # Exclude these from candidates, EXCEPT the test_item (which we want to retrieve)
+    orig_mask = original_data.user_ids == user_id
+    all_seen_items = set(original_data.item_ids[orig_mask])
+    exclude_items = all_seen_items - {test_item}
 
-    # Generate candidates
+    # Generate candidates using training profile, excluding seen items (except test)
     candidate_ids, relevance_scores = get_candidates(
-        profile_items, similarity, config.topk_similar_per_item, config.max_candidates
+        profile_items, similarity, config.topk_similar_per_item, config.max_candidates, exclude_items
     )
 
     if len(candidate_ids) < config.k:
diff --git a/benchmarks/results/amazon-video-games.json b/benchmarks/results/amazon-video-games.json
index 9361f3c..395fcb8 100644
--- a/benchmarks/results/amazon-video-games.json
+++ b/benchmarks/results/amazon-video-games.json
@@ -1,6 +1,6 @@
 {
   "dataset": "amazon-video-games",
-  "timestamp": "2026-01-25T11:44:58.210613+00:00",
+  "timestamp": "2026-01-26T11:20:46.729050+00:00",
   "config": {
     "n_users": 707,
     "n_items": 537,
@@ -17,3540 +17,3540 @@
       {
         "strategy": "dpp",
         "diversity": 0.0,
-        "mrr": 0.23228822882288228,
-        "ndcg@10": 0.2790212189683253,
-        "ilad": 0.7105413888181958,
-        "ilmd": 0.2455601963666406
+        "mrr": 0.051654093980826654,
+        "ndcg@10": 0.07226743164339795,
+        "ilad": 0.7137441910813856,
+        "ilmd": 0.22501837760420804
       },
       {
         "strategy": "dpp",
         "diversity": 0.1,
-        "mrr": 0.24022080779506522,
-        "ndcg@10": 0.2891892107146568,
-        "ilad": 0.7398682728599456,
-        "ilmd": 0.29090181899374246
+        "mrr": 0.05143968478480502,
+        "ndcg@10": 0.07263061790448247,
+        "ilad": 0.7466213684186571,
+        "ilmd": 0.27611554520470755
       },
       {
         "strategy": "dpp",
         "diversity": 0.2,
-        "mrr": 0.24096338205249096,
-        "ndcg@10": 0.2904184030580189,
-        "ilad": 0.7431993081512344,
-        "ilmd": 0.295381149780295
+        "mrr": 0.05189319952403403,
+        "ndcg@10": 0.07302096452506637,
+        "ilad": 0.750746919503475,
+        "ilmd": 0.28068160614501975
       },
       {
         "strategy": "dpp",
         "diversity": 0.3,
-        "mrr": 0.24146797332794506,
-        "ndcg@10": 0.2914447745878532,
-        "ilad": 0.7475329471865334,
-        "ilmd": 0.30226651605487376
+        "mrr": 0.051617610740665905,
+        "ndcg@10": 0.07279399603551041,
+        "ilad": 0.7560697510690972,
+        "ilmd": 0.28786619726299734
       },
       {
         "strategy": "dpp",
         "diversity": 0.4,
-        "mrr": 0.2418131609079275,
-        "ndcg@10": 0.29174199036871673,
-        "ilad": 0.753077340471694,
-        "ilmd": 0.3115877307624763
+        "mrr": 0.051967849846209106,
+        "ndcg@10": 0.07307831431424658,
+        "ilad": 0.7630301718580335,
+        "ilmd": 0.2972617971340703
       },
       {
         "strategy": "dpp",
         "diversity": 0.5,
-        "mrr": 0.24255012235917472,
-        "ndcg@10": 0.2918043722695043,
-        "ilad": 0.7614449542094828,
-        "ilmd": 0.32528375609760746
+        "mrr": 0.05235457219191307,
+        "ndcg@10": 0.07370733271003367,
+        "ilad": 0.7703432949547045,
+        "ilmd": 0.30997052995285185
       },
       {
         "strategy": "dpp",
         "diversity": 0.6,
-        "mrr": 0.2437563144069509,
-        "ndcg@10": 0.2937245596859424,
-        "ilad": 0.77179579207044,
-        "ilmd": 0.3439874860656986
+        "mrr": 0.052572349071641856,
+        "ndcg@10": 0.07420565016613388,
+        "ilad": 0.7835314221564244,
+        "ilmd": 0.33113143543542867
       },
       {
         "strategy": "dpp",
         "diversity": 0.7,
-        "mrr": 0.24636907568307853,
-        "ndcg@10": 0.2965082931174429,
-        "ilad": 0.7880115334097364,
-        "ilmd": 0.37037678376957245
+        "mrr": 0.052387126467748825,
+        "ndcg@10": 0.07312210676443999,
+        "ilad": 0.8010217361942544,
+        "ilmd": 0.3607192257000331
       },
       {
         "strategy": "dpp",
         "diversity": 0.8,
-        "mrr": 0.24790642329539075,
-        "ndcg@10": 0.29818124085336956,
-        "ilad": 0.8174925586458985,
-        "ilmd": 0.4172500453710219
+        "mrr": 0.05014873936373229,
+        "ndcg@10": 0.06824543049883587,
+        "ilad": 0.8302259319573851,
+        "ilmd": 0.4112866466925539
       },
       {
         "strategy": "dpp",
         "diversity": 0.9,
-        "mrr": 0.2532077187310568,
-        "ndcg@10": 0.30604162531719953,
-        "ilad": 0.875048992573119,
-        "ilmd": 0.5217687364514849
+        "mrr": 0.04519936687546306,
+        "ndcg@10": 0.060718651380761185,
+        "ilad": 0.888178338761714,
+        "ilmd": 0.5218811739114926
       },
       {
         "strategy": "dpp",
         "diversity": 1.0,
-        "mrr": 0.16760344401787117,
-        "ndcg@10": 0.17105421732221834,
-        "ilad": 0.9902785942274577,
-        "ilmd": 0.8856152248011621
+        "mrr": 0.02912485126063627,
+        "ndcg@10": 0.03477460082961605,
+        "ilad": 0.9924883055889286,
+        "ilmd": 0.8959774625351945
       },
       {
         "strategy": "mmr",
         "diversity": 0.0,
-        "mrr": 0.23228822882288228,
-        "ndcg@10": 0.2790212189683253,
-        "ilad": 0.7105413888181958,
-        "ilmd": 0.2455601963666406
+        "mrr": 0.051654093980826654,
+        "ndcg@10": 0.07226743164339795,
+        "ilad": 0.7137441910813856,
+        "ilmd": 0.22501837760420804
       },
       {
         "strategy": "mmr",
         "diversity": 0.1,
-        "mrr": 0.2361711681372219,
-        "ndcg@10": 0.284621131411053,
-        "ilad": 0.7203397834317856,
-        "ilmd": 0.2571816031295474
+        "mrr": 0.05137626007498709,
+        "ndcg@10": 0.07144455003015196,
+        "ilad": 0.7235144216151015,
+        "ilmd": 0.237010620186656
       },
       {
         "strategy": "mmr",
         "diversity": 0.2,
-        "mrr": 0.23962136009519322,
-        "ndcg@10": 0.2892952684375146,
-        "ilad": 0.7316462977182072,
-        "ilmd": 0.2730726538149606
+        "mrr": 0.05116297344020117,
+        "ndcg@10": 0.070967234332196,
+        "ilad": 0.7348559297716095,
+        "ilmd": 0.25665306547574984
       },
       {
         "strategy": "mmr",
         "diversity": 0.3,
-        "mrr": 0.24307716485934308,
-        "ndcg@10": 0.2937064016278286,
-        "ilad": 0.7445790509692007,
-        "ilmd": 0.2977379679342645
+        "mrr": 0.05108607799555465,
+        "ndcg@10": 0.07178460428257753,
+        "ilad": 0.752487021086071,
+        "ilmd": 0.27867423712448486
       },
       {
         "strategy": "mmr",
         "diversity": 0.4,
-        "mrr": 0.24520554096225952,
-        "ndcg@10": 0.2961226036574152,
-        "ilad": 0.7619860979927311,
-        "ilmd": 0.32374756879145555
+        "mrr": 0.05130217103342987,
+        "ndcg@10": 0.07162088348136468,
+        "ilad": 0.7749311989318195,
+        "ilmd": 0.31255334733736395
       },
       {
         "strategy": "mmr",
         "diversity": 0.5,
-        "mrr": 0.24744561190812958,
-        "ndcg@10": 0.2992327071417331,
-        "ilad": 0.788008840451652,
-        "ilmd": 0.364086535223134
+        "mrr": 0.050697671807997134,
+        "ndcg@10": 0.07138952181689727,
+        "ilad": 0.8045877281897813,
+        "ilmd": 0.3660336721904544
       },
       {
         "strategy": "mmr",
         "diversity": 0.6,
-        "mrr": 0.25104847219415816,
-        "ndcg@10": 0.30368781544104784,
-        "ilad": 0.8251297737365059,
-        "ilmd": 0.42638554195366285
+        "mrr": 0.050660627287218526,
+        "ndcg@10": 0.06958057882540733,
+        "ilad": 0.8416872681704741,
+        "ilmd": 0.4300695423358232
       },
       {
         "strategy": "mmr",
         "diversity": 0.7,
-        "mrr": 0.24072371522866573,
-        "ndcg@10": 0.2890174956623495,
-        "ilad": 0.8808217485352318,
-        "ilmd": 0.5328289969301291
+        "mrr": 0.04533407422374891,
+        "ndcg@10": 0.062392486711179376,
+        "ilad": 0.8980055794941822,
+        "ilmd": 0.5459099798087858
       },
       {
         "strategy": "mmr",
         "diversity": 0.8,
-        "mrr": 0.21982779910644126,
-        "ndcg@10": 0.25565789971508535,
-        "ilad": 0.9450697838164118,
-        "ilmd": 0.6861976150744706
+        "mrr": 0.03842302597606699,
+        "ndcg@10": 0.05199602841325359,
+        "ilad": 0.957828157018907,
+        "ilmd": 0.7172628416873441
       },
       {
         "strategy": "mmr",
         "diversity": 0.9,
-        "mrr": 0.19138434251588424,
-        "ndcg@10": 0.20638869933797016,
-        "ilad": 0.986993212763963,
-        "ilmd": 0.8490720376941405
+        "mrr": 0.035236074627870956,
+        "ndcg@10": 0.044418279055206705,
+        "ilad": 0.9931142297290171,
+        "ilmd": 0.871120582544146
       },
       {
         "strategy": "mmr",
         "diversity": 1.0,
-        "mrr": 0.1696678341303518,
-        "ndcg@10": 0.17272936972278066,
-        "ilad": 0.998142724411491,
-        "ilmd": 0.8971105883954438
+        "mrr": 0.03117577063828832,
+        "ndcg@10": 0.03612804978858595,
+        "ilad": 1.0021305950898598,
+        "ilmd": 0.9081911750696336
       },
       {
         "strategy": "msd",
         "diversity": 0.0,
-        "mrr": 0.23228822882288228,
-        "ndcg@10": 0.2790212189683253,
-        "ilad": 0.7105413888181958,
-        "ilmd": 0.2455601963666406
+        "mrr": 0.051654093980826654,
+        "ndcg@10": 0.07226743164339795,
+        "ilad": 0.7137441910813856,
+        "ilmd": 0.22501837760420804
       },
       {
         "strategy": "msd",
         "diversity": 0.1,
-        "mrr": 0.23872387238723872,
-        "ndcg@10": 0.2901046333880344,
-        "ilad": 0.7640429551817234,
-        "ilmd": 0.2718844956690722
+        "mrr": 0.05109842616914753,
+        "ndcg@10": 0.07232024669626147,
+        "ilad": 0.7734406871202137,
+        "ilmd": 0.25780770153102306
       },
       {
         "strategy": "msd",
         "diversity": 0.2,
-        "mrr": 0.24124402236141979,
-        "ndcg@10": 0.2916394676388019,
-        "ilad": 0.8121697514026809,
-        "ilmd": 0.3095969716258231
+        "mrr": 0.04967333468040682,
+        "ndcg@10": 0.0690260890724424,
+        "ilad": 0.8251277029598619,
+        "ilmd": 0.29541966230562416
       },
       {
         "strategy": "msd",
         "diversity": 0.3,
-        "mrr": 0.24094373723086596,
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 }
diff --git a/benchmarks/results/goodreads.json b/benchmarks/results/goodreads.json
index e1b0fc1..f38b57e 100644
--- a/benchmarks/results/goodreads.json
+++ b/benchmarks/results/goodreads.json
@@ -1,6 +1,6 @@
 {
   "dataset": "goodreads",
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+  "timestamp": "2026-01-26T11:30:36.995482+00:00",
   "config": {
     "n_users": 10279,
     "n_items": 20980,
@@ -17,3540 +17,3540 @@
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diff --git a/benchmarks/results/lastfm.json b/benchmarks/results/lastfm.json
index cbfd171..d549326 100644
--- a/benchmarks/results/lastfm.json
+++ b/benchmarks/results/lastfm.json
@@ -1,6 +1,6 @@
 {
   "dataset": "lastfm",
-  "timestamp": "2026-01-25T11:43:46.869133+00:00",
+  "timestamp": "2026-01-26T11:19:21.924938+00:00",
   "config": {
     "n_users": 1859,
     "n_items": 2823,
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diff --git a/benchmarks/results/ml-32m.json b/benchmarks/results/ml-32m.json
index e54c6cf..ed56f33 100644
--- a/benchmarks/results/ml-32m.json
+++ b/benchmarks/results/ml-32m.json
@@ -1,6 +1,6 @@
 {
   "dataset": "ml-32m",
-  "timestamp": "2026-01-25T11:37:23.521317+00:00",
+  "timestamp": "2026-01-26T11:13:25.894250+00:00",
   "config": {
     "n_users": 198973,
     "n_items": 25051,
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From 3e05de86178e1014a76a724067747b33022bb2fd Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 14:51:57 +0100
Subject: [PATCH 49/69] Updated results

---
 benchmarks/README.md | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 97d26b8..ce3a7aa 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -290,19 +290,18 @@ We use a **relevance floor** approach to ensure fair comparison:
 3. **Compare**: Among feasible configs, find which strategy achieves:
    - **Max ILAD**: Best overall diversity spread
    - **Max ILMD**: Best worst-case diversity (fewer similar pairs)
-   - **Best Overall**: Best 3-way score combining all metrics
+   - **Best Combined**: Best 2-way Diversity Score (ILAD × ILMD)
 
 We report results at two relevance floors:
 - **99%**: Near-zero relevance loss, production-safe
 - **95%**: Balanced tradeoff with more diversity headroom
 
-The **3-way Score** rewards strategies that improve *all three* metrics:
-- `nDCG_gain = normalized(nDCG / baseline_nDCG)`
-- `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_max - ILAD_baseline)`
-- `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_max - ILMD_baseline)`
-- `Score = ∛(nDCG_gain × ILAD_gain × ILMD_gain)` (geometric mean)
+The **Diversity Score** (used in Tables 2-3) measures balanced diversity improvement:
+- `ILAD_gain = (ILAD - ILAD_baseline) / (ILAD_feasible_max - ILAD_baseline)`
+- `ILMD_gain = (ILMD - ILMD_baseline) / (ILMD_feasible_max - ILMD_baseline)`
+- `Diversity Score = √(ILAD_gain × ILMD_gain)` (geometric mean)
 
-This ensures a strategy must improve relevance AND diversity to score well.
+This uses **feasible-max normalization**: the max is computed only over points meeting the relevance floor, ensuring fair comparison within each constraint level.
 
 ### Diversity Metrics
 

From fa7de7fbf4502892fe264ec4c17e01861cd78e9d Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 18:47:45 +0100
Subject: [PATCH 50/69] Updated readme

---
 README.md            | 14 +++++++-------
 benchmarks/README.md | 14 +++++---------
 2 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index 5d1b0fb..4c0dbd7 100644
--- a/README.md
+++ b/README.md
@@ -81,21 +81,21 @@ The following table describes the supported strategies, how they work, their tim
 
 We evaluated all strategies across 4 recommendation datasets (MovieLens-32M, Amazon Video Games, Goodreads, Last.FM). Full methodology and detailed results are in [`benchmarks/`](benchmarks/).
 
-**TL;DR: Use DPP with `diversity=0.7-0.9`** for best overall diversity gains with minimal relevance loss.
+**TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
 
 | Relevance Floor | ILAD Improvement | ILMD Improvement |
 |-----------------|:----------------:|:----------------:|
-| 99% (strict)    | +51%             | +104%            |
-| 95% (balanced)  | +79%             | +178%            |
+| 99% (strict)    | +44%             | +86%             |
+| 95% (balanced)  | +49%             | +99%             |
 
 **Quick recommendations:**
 
 | Use Case | Strategy | `diversity` |
 |----------|----------|:-----------:|
-| Best overall balance | **DPP** | 0.7–0.9 |
-| Maximum variety (ILAD) | MSD | 0.4–0.6 |
-| Fewer similar pairs (ILMD) | DPP | 0.7–0.9 |
-| Sequence-aware (feeds, RAG) | SSD | 0.7–0.9 |
+| Best overall balance | **DPP** | 0.6–0.8 |
+| Maximum variety (ILAD) | MSD | 0.4–0.5 |
+| Fewer similar pairs (ILMD) | DPP | 0.7–0.8 |
+| Sequence-aware (feeds, RAG) | SSD | 0.8–0.9 |
 
 → See [full benchmark results](benchmarks/) for per-dataset tables and Pareto plots.
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index ce3a7aa..8f45cff 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -5,7 +5,7 @@ This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**,
 For each dataset, we use a leave-one-out evaluation protocol: we hold out one item per user as the
 test set, train embeddings on the remaining interactions (preventing leakage), generate candidate items,
 then rerank with each diversification strategy. We measure relevance (nDCG) and diversity (ILAD, ILMD)
-to answer: *which strategy gives the best relevance-diversity tradeoff?*
+to answer: *(1) can diversification improve relevance? (2) how much diversity can we gain without hurting it?*
 
 In addition, we measure latency of each strategy as the number of candidates scales.
 
@@ -49,8 +49,6 @@ Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
 
 *nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results averaged across 4 datasets, 10 runs.*
 
-**DPP leads**, achieving +3.1% nDCG improvement while also boosting ILAD by 26% and ILMD by 54%.
-
 ### Table 2: Diversity Leaderboard (≥99% Relevance)
 
 Best diversity per strategy while maintaining ≥99% of baseline nDCG, ranked by diversity score:
@@ -66,7 +64,7 @@ Best diversity per strategy while maintaining ≥99% of baseline nDCG, ranked by
 
 ### Table 3: Diversity Leaderboard (≥95% Relevance)
 
-Best diversity per strategy while maintaining ≥95% of baseline nDCG:
+Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by diversity score:
 
 | Strategy | Diversity Score | nDCG Δ | ILAD (+%) | ILMD (+%) | `diversity` |
 |----------|:---------------:|:------:|:---------:|:---------:|:-----------:|
@@ -75,13 +73,11 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG:
 | SSD      | 0.537 | +1.5% | +31% | +49% | 0.8 |
 | MSD      | 0.485 | -2.3% | +54% | +24% | 0.4 |
 
-*With a looser floor, you can push diversity higher—but DPP still leads with positive nDCG gains.*
-
 ### Summary
 
-- **DPP wins on all three leaderboards**: best accuracy, best diversity under both 99% and 95% floors
-- **At strict 99% floor**: DPP achieves +44% ILAD and +86% ILMD while still *improving* nDCG (+1.8%)
-- **MSD** excels at pushing ILAD very high, but at the cost of ILMD and sometimes relevance
+- **DPP wins all three leaderboards** — best accuracy *and* best diversity at both 99% and 95% floors
+- At 99% floor: +44% ILAD, +86% ILMD, *and* +1.8% nDCG (diversity improves relevance)
+- **MSD** maximizes ILAD but sacrifices ILMD and sometimes relevance
 
 ### Recommendations
 

From eba8cac41b779c771cbacd03cacbd24e829e6ad8 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 18:55:17 +0100
Subject: [PATCH 51/69] Updated readme

---
 benchmarks/README.md | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 8f45cff..17f750b 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -32,7 +32,7 @@ We answer two questions:
 
 We measure two diversity metrics:
 - **ILAD** (Intra-List Average Distance): average pairwise diversity—higher means more variety
-- **ILMD** (Intra-List Minimum Distance): minimum pairwise diversity—higher means fewer similar pairs
+- **ILMD** (Intra-List Minimum Distance): minimum pairwise diversity—higher means better worst-case diversity (fewer similar pairs)
 
 **Why can diversification improve nDCG?** In offline leave-one-out evaluation, the "baseline" ranking (no diversification) often places many near-duplicate items at the top. Diversifiers act as intelligent tie-breakers: by spreading selections across different item neighborhoods, they increase the chance of including the held-out test item. This is why moderate diversification can *improve* relevance, not just diversity.
 
@@ -47,7 +47,7 @@ Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
 | MMR      | +1.5% | +11% | +17% | 0.4 |
 | MSD      | +1.0% | +17% | +5% | 0.2 |
 
-*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results averaged across 4 datasets, 10 runs.*
+*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results are macro-averages: for each dataset, we find the best `diversity` for each strategy, then average those per-dataset bests. 10 runs per dataset.*
 
 ### Table 2: Diversity Leaderboard (≥99% Relevance)
 
@@ -112,14 +112,14 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by
 
 ## Latency
 
-All strategies are extremely fast in practice—even with 10,000 candidates, all complete in <100ms. The plot below shows latency scaling with the number of candidates (k=10, d=256):
+All strategies are extremely fast in practice—even with 10,000 candidates, all complete in <100ms. The plot below shows latency scaling measured on a **separate synthetic benchmark** (k=10, d=256 embeddings, typical for modern embedding models):
 
 ![Latency vs Candidates](results/latency.png)
 
 **Key observations:**
 - **All strategies are very fast**: Even with 10,000 candidates, all strategies complete in under 100ms
 - **Typical use case**: With 100 candidates (common for reranking), all strategies complete in <1ms
-- **MMR/MSD/DPP** are nearly identical in speed for practical candidate sizes
+- **MMR/MSD/DPP** are same order of magnitude; DPP is modestly slower at scale
 - **SSD** is slower due to Gram-Schmidt orthogonalization—scales with embedding dimension d
 
 | Strategy | 100 candidates | 1,000 candidates | 10,000 candidates |
@@ -129,7 +129,7 @@ All strategies are extremely fast in practice—even with 10,000 candidates, all
 | DPP | ~0.1ms | ~2ms | ~20ms |
 | SSD | ~0.5ms | ~5ms | ~80ms |
 
-*Measured with k=10 items selected, d=256 dimensional embeddings.*
+*Measured with k=10 items selected, d=256 dimensional embeddings. Note: main benchmarks use d=64; latency benchmark uses d=256 to reflect modern embedding model dimensions.*
 
 ## Usage
 
@@ -173,7 +173,7 @@ python -m benchmarks report
 <details>
 <summary>Per-Strategy Diversity Sweep (Averaged Across Datasets)</summary>
 
-Shows how each strategy's metrics change as you increase `diversity`, averaged across all 4 datasets.
+Shows how each strategy's metrics change as you increase `diversity`, **averaged across all 4 datasets**. Baseline values (ILAD=0.42, ILMD=0.12 at `diversity=0`) are the cross-dataset averages.
 
 > **Note:** nDCG Retention can exceed 100% and may not decrease monotonically. This happens because
 > diversification can sometimes *improve* relevance by breaking ties among similar items or surfacing
@@ -265,8 +265,8 @@ Shows how each strategy's metrics change as you increase `diversity`, averaged a
 
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
 - **Runs**: 10 runs per dataset with different random seeds for robust results (20,000 total evaluations)
-- **Embeddings**: 64-dim SVD on item co-occurrence matrix, **trained on training data only** (held-out items excluded to prevent leakage)
-- **Candidates**: Top-100 similar items per profile item
+- **Embeddings**: 64-dim truncated SVD on the item co-occurrence matrix (training interactions only; held-out edges excluded)
+- **Candidates**: For each user, we take the union of top-50 similar items per profile item (via cosine similarity), then score candidates by mean similarity to the profile. The held-out item is eligible; other already-interacted items are excluded
 - **Selection**: k=10 items selected by each strategy
 - **`diversity` sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
@@ -303,8 +303,9 @@ This uses **feasible-max normalization**: the max is computed only over points m
 
 | Metric | Type | Description |
 |--------|------|-------------|
-| **ILAD** | Average | Mean pairwise distance—measures overall variety |
-| **ILMD** | Minimum | Min pairwise distance—higher = fewer similar pairs |
+| **nDCG@10** | Relevance | Normalized Discounted Cumulative Gain at position 10 |
+| **ILAD** | Average diversity | Mean pairwise distance—measures overall variety |
+| **ILMD** | Minimum diversity | Min pairwise distance—higher = better worst-case diversity |
 
 Both are computed as `1 - cosine_similarity` between item embeddings.
 

From 0fa4d4dce2f3c3f98bbf1c80e036c8cdf36557e1 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 19:09:26 +0100
Subject: [PATCH 52/69] Improved writing

---
 README.md            |  2 +-
 benchmarks/README.md | 44 +++++++++++++++++++++-----------------------
 2 files changed, 22 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 4c0dbd7..e6d51f6 100644
--- a/README.md
+++ b/README.md
@@ -79,7 +79,7 @@ The following table describes the supported strategies, how they work, their tim
 
 ## Benchmarks
 
-We evaluated all strategies across 4 recommendation datasets (MovieLens-32M, Amazon Video Games, Goodreads, Last.FM). Full methodology and detailed results are in [`benchmarks/`](benchmarks/).
+All strategies evaluated across 4 recommendation datasets (MovieLens-32M, Amazon Video Games, Goodreads, Last.FM). Full methodology and detailed results in [`benchmarks/`](benchmarks/).
 
 **TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 17f750b..a4c0fe6 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -1,15 +1,15 @@
 # Pyversity Benchmarks
 
-This directory contains comprehensive benchmarks for **MMR**, **MSD**, **DPP**, and **SSD** across 4 recommendation datasets.
+Benchmarks for **MMR**, **MSD**, **DPP**, and **SSD** across 4 recommendation datasets.
 
-For each dataset, we use a leave-one-out evaluation protocol: we hold out one item per user as the
-test set, train embeddings on the remaining interactions (preventing leakage), generate candidate items,
-then rerank with each diversification strategy. We measure relevance (nDCG) and diversity (ILAD, ILMD)
-to answer: *(1) can diversification improve relevance? (2) how much diversity can we gain without hurting it?*
+Each dataset uses a leave-one-out evaluation: one item per user is held out as the test set, embeddings
+are trained on the remaining interactions (preventing leakage), candidate items are generated, then
+reranked with each diversification strategy. Relevance (nDCG) and diversity (ILAD, ILMD) are measured
+to answer: *(1) can diversification improve relevance? (2) how much diversity can you gain without hurting it?*
 
-In addition, we measure latency of each strategy as the number of candidates scales.
+Latency is also measured as the number of candidates scales.
 
-> **Note:** We don't benchmark COVER (coverage-based diversification) because it optimizes a different
+> **Note:** COVER (coverage-based diversification) is not included because it optimizes a different
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard
 > collaborative filtering datasets.
 
@@ -25,12 +25,12 @@ In addition, we measure latency of each strategy as the number of candidates sca
 
 ## Key Findings
 
-We answer two questions:
+The results answer two questions:
 
 1. **Accuracy**: Which strategy achieves the best relevance (nDCG)?
-2. **Diversity under constraint**: How much diversity can we gain while maintaining relevance?
+2. **Diversity under constraint**: How much diversity can you gain while maintaining relevance?
 
-We measure two diversity metrics:
+Diversity is measured with two metrics:
 - **ILAD** (Intra-List Average Distance): average pairwise diversity—higher means more variety
 - **ILMD** (Intra-List Minimum Distance): minimum pairwise diversity—higher means better worst-case diversity (fewer similar pairs)
 
@@ -47,7 +47,7 @@ Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
 | MMR      | +1.5% | +11% | +17% | 0.4 |
 | MSD      | +1.0% | +17% | +5% | 0.2 |
 
-*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results are macro-averages: for each dataset, we find the best `diversity` for each strategy, then average those per-dataset bests. 10 runs per dataset.*
+*nDCG Δ and ILAD/ILMD show % change vs baseline (`diversity=0`). Results are macro-averages: the best `diversity` is found per strategy per dataset, then averaged. 10 runs per dataset.*
 
 ### Table 2: Diversity Leaderboard (≥99% Relevance)
 
@@ -112,15 +112,13 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by
 
 ## Latency
 
-All strategies are extremely fast in practice—even with 10,000 candidates, all complete in <100ms. The plot below shows latency scaling measured on a **separate synthetic benchmark** (k=10, d=256 embeddings, typical for modern embedding models):
+All strategies are fast. Even with 10,000 candidates, all complete in <100ms. The plot below shows latency scaling measured on a **separate synthetic benchmark** (k=10, d=256 embeddings, typical for modern embedding models):
 
 ![Latency vs Candidates](results/latency.png)
 
-**Key observations:**
-- **All strategies are very fast**: Even with 10,000 candidates, all strategies complete in under 100ms
-- **Typical use case**: With 100 candidates (common for reranking), all strategies complete in <1ms
+- **Typical use case** (100 candidates): all strategies complete in <1ms
 - **MMR/MSD/DPP** are same order of magnitude; DPP is modestly slower at scale
-- **SSD** is slower due to Gram-Schmidt orthogonalization—scales with embedding dimension d
+- **SSD** is slower due to Gram-Schmidt orthogonalization, which scales with embedding dimension
 
 | Strategy | 100 candidates | 1,000 candidates | 10,000 candidates |
 |----------|----------------|------------------|-------------------|
@@ -266,29 +264,29 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
 - **Runs**: 10 runs per dataset with different random seeds for robust results (20,000 total evaluations)
 - **Embeddings**: 64-dim truncated SVD on the item co-occurrence matrix (training interactions only; held-out edges excluded)
-- **Candidates**: For each user, we take the union of top-50 similar items per profile item (via cosine similarity), then score candidates by mean similarity to the profile. The held-out item is eligible; other already-interacted items are excluded
+- **Candidates**: For each user, the union of top-50 similar items per profile item (via cosine similarity), scored by mean similarity to the profile. The held-out item is eligible; other already-interacted items are excluded
 - **Selection**: k=10 items selected by each strategy
 - **`diversity` sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
-> **Evaluation setting**: We evaluate diversification as a reranking step in a transductive next-item
+> **Evaluation setting**: Diversification is evaluated as a reranking step in a transductive next-item
 > prediction setting. Item embeddings and candidate generation are learned from the interaction graph
 > with the specific held-out edges removed; rerankers are compared on the resulting candidate lists.
-> The held-out item is eligible as a candidate (we're trying to retrieve it), but other already-interacted
-> items are excluded. This setup isolates the diversification question: "given fixed candidates and scores,
+> The held-out item is eligible as a candidate (the goal is to retrieve it), but other already-interacted
+> items are excluded. This isolates the diversification question: "given fixed candidates and scores,
 > which reranking strategy produces the best relevance-diversity tradeoff?"
 
 ### Relevance-Budgeted Evaluation
 
-We use a **relevance floor** approach to ensure fair comparison:
+A **relevance floor** ensures fair comparison:
 
-1. **Baseline**: For each dataset, compute baseline nDCG at `diversity=0` (no diversification)
+1. **Baseline**: Compute baseline nDCG at `diversity=0` (no diversification) for each dataset
 2. **Filter**: Keep only configs where nDCG meets the relevance floor
 3. **Compare**: Among feasible configs, find which strategy achieves:
    - **Max ILAD**: Best overall diversity spread
    - **Max ILMD**: Best worst-case diversity (fewer similar pairs)
    - **Best Combined**: Best 2-way Diversity Score (ILAD × ILMD)
 
-We report results at two relevance floors:
+Results are reported at two relevance floors:
 - **99%**: Near-zero relevance loss, production-safe
 - **95%**: Balanced tradeoff with more diversity headroom
 

From 26eb73320049c4ebdc573f8ecb191464ca9cc8c7 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 19:20:11 +0100
Subject: [PATCH 53/69] Improved writing

---
 benchmarks/README.md | 49 ++++++++++++++++++++++----------------------
 1 file changed, 24 insertions(+), 25 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index a4c0fe6..6f8caa4 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -5,7 +5,7 @@ Benchmarks for **MMR**, **MSD**, **DPP**, and **SSD** across 4 recommendation da
 Each dataset uses a leave-one-out evaluation: one item per user is held out as the test set, embeddings
 are trained on the remaining interactions (preventing leakage), candidate items are generated, then
 reranked with each diversification strategy. Relevance (nDCG) and diversity (ILAD, ILMD) are measured
-to answer: *(1) can diversification improve relevance? (2) how much diversity can you gain without hurting it?*
+to answer: *(1) how much can diversification improve relevance? (2) what's the relevance-diversity tradeoff?*
 
 Latency is also measured as the number of candidates scales.
 
@@ -14,16 +14,15 @@ Latency is also measured as the number of candidates scales.
 > collaborative filtering datasets.
 
 ## Table of Contents
-- [Key Findings](#key-findings)
-- [Relevance-Diversity Tradeoff](#relevance-diversity-tradeoff)
+- [Key Results](#key-results)
+- [Plots](#plots)
 - [Latency](#latency)
 - [Detailed Results](#detailed-results)
-- [Usage](#usage)
-- [Datasets](#datasets)
 - [Methodology](#methodology)
+- [Usage](#usage)
 - [Citations](#citations)
 
-## Key Findings
+## Key Results
 
 The results answer two questions:
 
@@ -34,8 +33,6 @@ Diversity is measured with two metrics:
 - **ILAD** (Intra-List Average Distance): average pairwise diversity—higher means more variety
 - **ILMD** (Intra-List Minimum Distance): minimum pairwise diversity—higher means better worst-case diversity (fewer similar pairs)
 
-**Why can diversification improve nDCG?** In offline leave-one-out evaluation, the "baseline" ranking (no diversification) often places many near-duplicate items at the top. Diversifiers act as intelligent tie-breakers: by spreading selections across different item neighborhoods, they increase the chance of including the held-out test item. This is why moderate diversification can *improve* relevance, not just diversity.
-
 ### Table 1: Accuracy Leaderboard
 
 Each strategy's best nDCG (selecting the `diversity` that maximizes relevance):
@@ -96,7 +93,7 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by
 > recently shown items. In content feeds with sliding windows, SSD's novelty-relative-to-recent
 > approach should yield larger effective diversity gains than shown here.
 
-## Relevance-Diversity Tradeoff
+## Plots
 
 ### ILAD (Average Diversity)
 
@@ -129,19 +126,6 @@ All strategies are fast. Even with 10,000 candidates, all complete in <100ms. Th
 
 *Measured with k=10 items selected, d=256 dimensional embeddings. Note: main benchmarks use d=64; latency benchmark uses d=256 to reflect modern embedding model dimensions.*
 
-## Usage
-
-```bash
-# Download datasets
-python -m benchmarks download
-
-# Run benchmarks
-python -m benchmarks run
-
-# Generate report
-python -m benchmarks report
-```
-
 ## Detailed Results
 
 <details>
@@ -248,7 +232,9 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 
 
 
-## Datasets
+## Methodology
+
+### Datasets
 
 | Dataset | Domain | Interactions | Source |
 |---------|--------|--------------|--------|
@@ -257,8 +243,6 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 | Amazon Video Games | Games | 47K reviews | [UCSD](https://cseweb.ucsd.edu/~jmcauley/datasets.html) |
 | Goodreads | Books | 869K ratings | [UCSD](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/) |
 
-## Methodology
-
 ### Experimental Setup
 
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
@@ -275,6 +259,8 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 > items are excluded. This isolates the diversification question: "given fixed candidates and scores,
 > which reranking strategy produces the best relevance-diversity tradeoff?"
 
+**Why can diversification improve nDCG?** In leave-one-out evaluation, the baseline ranking (no diversification) often places many near-duplicate items at the top. Diversifiers act as intelligent tie-breakers: by spreading selections across different item neighborhoods, they increase the chance of including the held-out test item. This is why moderate diversification can *improve* relevance, not just diversity.
+
 ### Relevance-Budgeted Evaluation
 
 A **relevance floor** ensures fair comparison:
@@ -325,6 +311,19 @@ results = run_benchmark(config)
 
 </details>
 
+## Usage
+
+```bash
+# Download datasets
+python -m benchmarks download
+
+# Run benchmarks
+python -m benchmarks run
+
+# Generate report
+python -m benchmarks report
+```
+
 ## Citations
 
 ```bibtex

From 7e6fa9d6fa0eb7f89b47ced7892b5a155476336b Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 19:25:29 +0100
Subject: [PATCH 54/69] Improved writing

---
 benchmarks/README.md | 52 ++++++++++++++++++++++----------------------
 1 file changed, 26 insertions(+), 26 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 6f8caa4..14f9fa6 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -243,6 +243,16 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 | Amazon Video Games | Games | 47K reviews | [UCSD](https://cseweb.ucsd.edu/~jmcauley/datasets.html) |
 | Goodreads | Books | 869K ratings | [UCSD](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/) |
 
+### Metrics
+
+| Metric | Type | Description |
+|--------|------|-------------|
+| **nDCG@10** | Relevance | Normalized Discounted Cumulative Gain at position 10 |
+| **ILAD** | Average diversity | Mean pairwise distance—measures overall variety |
+| **ILMD** | Minimum diversity | Min pairwise distance—higher = better worst-case diversity |
+
+ILAD and ILMD are computed as `1 - cosine_similarity` between item embeddings.
+
 ### Experimental Setup
 
 - **Evaluation**: Leave-one-out protocol with up to 2,000 sampled users per dataset (all users if fewer)
@@ -252,15 +262,15 @@ Shows how each strategy's metrics change as you increase `diversity`, **averaged
 - **Selection**: k=10 items selected by each strategy
 - **`diversity` sweep**: 0.0, 0.1, 0.2, ..., 0.9, 1.0
 
-> **Evaluation setting**: Diversification is evaluated as a reranking step in a transductive next-item
-> prediction setting. Item embeddings and candidate generation are learned from the interaction graph
-> with the specific held-out edges removed; rerankers are compared on the resulting candidate lists.
-> The held-out item is eligible as a candidate (the goal is to retrieve it), but other already-interacted
-> items are excluded. This isolates the diversification question: "given fixed candidates and scores,
-> which reranking strategy produces the best relevance-diversity tradeoff?"
+<details>
+<summary>Evaluation details</summary>
+
+Diversification is evaluated as a reranking step in a transductive next-item prediction setting. Item embeddings and candidate generation are learned from the interaction graph with the specific held-out edges removed; rerankers are compared on the resulting candidate lists. The held-out item is eligible as a candidate (the goal is to retrieve it), but other already-interacted items are excluded. This isolates the diversification question: "given fixed candidates and scores, which reranking strategy produces the best relevance-diversity tradeoff?"
 
 **Why can diversification improve nDCG?** In leave-one-out evaluation, the baseline ranking (no diversification) often places many near-duplicate items at the top. Diversifiers act as intelligent tie-breakers: by spreading selections across different item neighborhoods, they increase the chance of including the held-out test item. This is why moderate diversification can *improve* relevance, not just diversity.
 
+</details>
+
 ### Relevance-Budgeted Evaluation
 
 A **relevance floor** ensures fair comparison:
@@ -283,15 +293,18 @@ The **Diversity Score** (used in Tables 2-3) measures balanced diversity improve
 
 This uses **feasible-max normalization**: the max is computed only over points meeting the relevance floor, ensuring fair comparison within each constraint level.
 
-### Diversity Metrics
+## Usage
 
-| Metric | Type | Description |
-|--------|------|-------------|
-| **nDCG@10** | Relevance | Normalized Discounted Cumulative Gain at position 10 |
-| **ILAD** | Average diversity | Mean pairwise distance—measures overall variety |
-| **ILMD** | Minimum diversity | Min pairwise distance—higher = better worst-case diversity |
+```bash
+# Download datasets
+python -m benchmarks download
 
-Both are computed as `1 - cosine_similarity` between item embeddings.
+# Run benchmarks
+python -m benchmarks run
+
+# Generate report
+python -m benchmarks report
+```
 
 <details>
 <summary>Programmatic API</summary>
@@ -311,19 +324,6 @@ results = run_benchmark(config)
 
 </details>
 
-## Usage
-
-```bash
-# Download datasets
-python -m benchmarks download
-
-# Run benchmarks
-python -m benchmarks run
-
-# Generate report
-python -m benchmarks report
-```
-
 ## Citations
 
 ```bibtex

From 18fcee9caac9e915fa1724a9187aa8e1cf40b3ca Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 19:30:36 +0100
Subject: [PATCH 55/69] Improved writing

---
 benchmarks/README.md | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 14f9fa6..0768ae8 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -6,9 +6,10 @@ Each dataset uses a leave-one-out evaluation: one item per user is held out as t
 are trained on the remaining interactions (preventing leakage), candidate items are generated, then
 reranked with each diversification strategy. Relevance (nDCG) and diversity (ILAD, ILMD) are measured
 to answer: *(1) how much can diversification improve relevance? (2) what's the relevance-diversity tradeoff?*
-
 Latency is also measured as the number of candidates scales.
 
+**TL;DR: DPP wins on all metrics**: best accuracy and best diversity at both relevance floors.
+
 > **Note:** COVER (coverage-based diversification) is not included because it optimizes a different
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard
 > collaborative filtering datasets.
@@ -70,12 +71,6 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by
 | SSD      | 0.537 | +1.5% | +31% | +49% | 0.8 |
 | MSD      | 0.485 | -2.3% | +54% | +24% | 0.4 |
 
-### Summary
-
-- **DPP wins all three leaderboards** — best accuracy *and* best diversity at both 99% and 95% floors
-- At 99% floor: +44% ILAD, +86% ILMD, *and* +1.8% nDCG (diversity improves relevance)
-- **MSD** maximizes ILAD but sacrifices ILMD and sometimes relevance
-
 ### Recommendations
 
 | Goal | Strategy | `diversity` | Notes |

From cc65ee123f1e4beac15fdba9e8c68ecb58d6033f Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:14:19 +0100
Subject: [PATCH 56/69] Improved writing

---
 README.md            |  9 +--------
 benchmarks/README.md | 17 -----------------
 2 files changed, 1 insertion(+), 25 deletions(-)

diff --git a/README.md b/README.md
index e6d51f6..8ebf59b 100644
--- a/README.md
+++ b/README.md
@@ -79,17 +79,10 @@ The following table describes the supported strategies, how they work, their tim
 
 ## Benchmarks
 
-All strategies evaluated across 4 recommendation datasets (MovieLens-32M, Amazon Video Games, Goodreads, Last.FM). Full methodology and detailed results in [`benchmarks/`](benchmarks/).
+All strategies are evaluated across 4 recommendation datasets. Full methodology and detailed results in [`benchmarks/`](benchmarks/).
 
 **TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
 
-| Relevance Floor | ILAD Improvement | ILMD Improvement |
-|-----------------|:----------------:|:----------------:|
-| 99% (strict)    | +44%             | +86%             |
-| 95% (balanced)  | +49%             | +99%             |
-
-**Quick recommendations:**
-
 | Use Case | Strategy | `diversity` |
 |----------|----------|:-----------:|
 | Best overall balance | **DPP** | 0.6–0.8 |
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 0768ae8..1eddb9d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -71,23 +71,6 @@ Best diversity per strategy while maintaining ≥95% of baseline nDCG, ranked by
 | SSD      | 0.537 | +1.5% | +31% | +49% | 0.8 |
 | MSD      | 0.485 | -2.3% | +54% | +24% | 0.4 |
 
-### Recommendations
-
-| Goal | Strategy | `diversity` | Notes |
-|------|----------|:-----------:|-------|
-| **Best overall** | **DPP** | 0.6-0.8 | Best on all leaderboards; improves relevance AND diversity |
-| **Maximum variety (ILAD)** | **MSD** | 0.4-0.5 | Best ILAD, but worse ILMD and may lose relevance |
-| **Minimize similar pairs (ILMD)** | **DPP** | 0.7-0.8 | Best ILMD with strong ILAD too |
-| **Sequence-aware feeds** | **SSD** | 0.8-0.9 | Use with `recent_embeddings` |
-| **Simple implementation** | **MMR** | 0.6-0.7 | Easiest to implement, good results |
-
-*`diversity=0` prioritizes relevance, `diversity=1` prioritizes diversity.*
-
-> **Note on SSD:** These benchmarks evaluate single-batch diversification. SSD is designed for
-> **sequence-aware** diversification with `recent_embeddings`—it rewards novelty relative to
-> recently shown items. In content feeds with sliding windows, SSD's novelty-relative-to-recent
-> approach should yield larger effective diversity gains than shown here.
-
 ## Plots
 
 ### ILAD (Average Diversity)

From 60a12a8d68af9195b3b51f88c2e7b9be40e8c4c8 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:16:29 +0100
Subject: [PATCH 57/69] Improved writing

---
 README.md            | 7 +++----
 benchmarks/README.md | 4 ++--
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 8ebf59b..af5e4b9 100644
--- a/README.md
+++ b/README.md
@@ -79,10 +79,12 @@ The following table describes the supported strategies, how they work, their tim
 
 ## Benchmarks
 
-All strategies are evaluated across 4 recommendation datasets. Full methodology and detailed results in [`benchmarks/`](benchmarks/).
+All strategies are evaluated across 4 recommendation datasets. Full methodology and detailed results can be found in [`benchmarks`](benchmarks/).
 
 **TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
 
+### Recommendations
+
 | Use Case | Strategy | `diversity` |
 |----------|----------|:-----------:|
 | Best overall balance | **DPP** | 0.6–0.8 |
@@ -90,9 +92,6 @@ All strategies are evaluated across 4 recommendation datasets. Full methodology
 | Fewer similar pairs (ILMD) | DPP | 0.7–0.8 |
 | Sequence-aware (feeds, RAG) | SSD | 0.8–0.9 |
 
-→ See [full benchmark results](benchmarks/) for per-dataset tables and Pareto plots.
-
-
 ## Motivation
 
 Traditional retrieval systems rank results purely by relevance (how closely each item matches the query). While effective, this can lead to redundancy: top results often look nearly identical, which can create a poor user experience.
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 1eddb9d..76dff7c 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -15,7 +15,7 @@ Latency is also measured as the number of candidates scales.
 > collaborative filtering datasets.
 
 ## Table of Contents
-- [Key Results](#key-results)
+- [Main Results](#main-results)
 - [Plots](#plots)
 - [Latency](#latency)
 - [Detailed Results](#detailed-results)
@@ -23,7 +23,7 @@ Latency is also measured as the number of candidates scales.
 - [Usage](#usage)
 - [Citations](#citations)
 
-## Key Results
+## Main Results
 
 The results answer two questions:
 

From 69e960c39b20c1132f8e992a1d8bfb797d619bc5 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:21:18 +0100
Subject: [PATCH 58/69] Updated readme

---
 README.md | 19 +++++--------------
 1 file changed, 5 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index af5e4b9..d55975d 100644
--- a/README.md
+++ b/README.md
@@ -71,11 +71,11 @@ The following table describes the supported strategies, how they work, their tim
 
 | Strategy                              | What It Does                                                                                   | Time Complexity           | When to Use                                                                                    |
 | ------------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------- | ---------------------------------------------------------------------------------------------- |
-| **MMR** (Maximal Marginal Relevance)  | Keeps the most relevant items while down-weighting those too similar to what’s already picked. | **O(k · n · d)**          | Good default. Fast, simple, and works well when you just want to avoid near-duplicates.    |
-| **MSD** (Max Sum of Distances)        | Prefers items that are both relevant and far from *all* previous selections.                   | **O(k · n · d)**          | Use when you want stronger spread, i.e. results that cover a wider range of topics or styles.      |
-| **DPP** (Determinantal Point Process) | Samples diverse yet relevant items using probabilistic “repulsion.”                            | **O(k · n · d + n · k²)** | Ideal when you want to eliminate redundancy or ensure diversity is built-in to selection.  |
-| **COVER** (Facility-Location)         | Ensures selected items collectively represent the full dataset’s structure.                    | **O(k · n²)**             | Great for topic coverage or clustering scenarios, but slower for large `n`. |
-| **SSD** (Sliding Spectrum Decomposition) | Sequence‑aware diversification: rewards novelty relative to recently shown items.     | **O(k · n · d)**          | Great for content feeds & infinite scroll, e.g. social/news/product feeds where users consume sequentially, as well as conversational RAG to avoid showing similar chunks within the recent window.
+| **MMR** (Maximal Marginal Relevance)  | Keeps the most relevant items while down-weighting those too similar to what's already picked. | **O(k · n · d)**          | Simple baseline. Fast, easy to implement, competitive results. Recommended `diversity`: 0.6–0.7 |
+| **MSD** (Max Sum of Distances)        | Prefers items that are both relevant and far from *all* previous selections.                   | **O(k · n · d)**          | Best for maximum variety (ILAD), but may sacrifice relevance. Recommended `diversity`: 0.4–0.5 |
+| **DPP** (Determinantal Point Process) | Samples diverse yet relevant items using probabilistic "repulsion."                            | **O(k · n · d + n · k²)** | **Recommended default.** Best overall in benchmarks: highest accuracy and diversity. Recommended `diversity`: 0.6–0.8 |
+| **COVER** (Facility-Location)         | Ensures selected items collectively represent the full dataset's structure.                    | **O(k · n²)**             | Great for topic coverage or clustering scenarios, but slower for large `n`. |
+| **SSD** (Sliding Spectrum Decomposition) | Sequence‑aware diversification: rewards novelty relative to recently shown items.     | **O(k · n · d)**          | Great for content feeds & infinite scroll where users consume sequentially. Recommended `diversity`: 0.8–0.9 |
 
 ## Benchmarks
 
@@ -83,15 +83,6 @@ All strategies are evaluated across 4 recommendation datasets. Full methodology
 
 **TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
 
-### Recommendations
-
-| Use Case | Strategy | `diversity` |
-|----------|----------|:-----------:|
-| Best overall balance | **DPP** | 0.6–0.8 |
-| Maximum variety (ILAD) | MSD | 0.4–0.5 |
-| Fewer similar pairs (ILMD) | DPP | 0.7–0.8 |
-| Sequence-aware (feeds, RAG) | SSD | 0.8–0.9 |
-
 ## Motivation
 
 Traditional retrieval systems rank results purely by relevance (how closely each item matches the query). While effective, this can lead to redundancy: top results often look nearly identical, which can create a poor user experience.

From 70e72f32d6b38eb3e96f3c3734fcd78bf3203971 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:23:00 +0100
Subject: [PATCH 59/69] Updated readme

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index d55975d..bdf8d34 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@
 Pyversity is a fast, lightweight library for diversifying retrieval results.
 Retrieval systems often return highly similar items. Pyversity efficiently re-ranks these results to encourage diversity, surfacing items that remain relevant but less redundant.
 
-It implements several popular diversification strategies such as MMR, MSD, DPP, and Cover with a clear, unified API. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
+It implements several popular diversification strategies such as MMR, MSD, DPP, SSD, and Cover with a clear, unified API. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
 
 
 ## Quickstart
@@ -53,7 +53,7 @@ diversified_result = diversify(
     embeddings=embeddings,
     scores=scores,
     k=10, # Number of items to select
-    strategy=Strategy.MMR, # Diversification strategy to use
+    strategy=Strategy.DPP, # Diversification strategy to use
     diversity=0.5 # Diversity parameter (higher values prioritize diversity)
 )
 

From 3c431691bb56810e0d44c1ef38fa06fbb9e25251 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:31:56 +0100
Subject: [PATCH 60/69] Updated readme

---
 README.md            | 10 +++++-----
 benchmarks/README.md |  2 --
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index bdf8d34..0f7edd4 100644
--- a/README.md
+++ b/README.md
@@ -71,17 +71,17 @@ The following table describes the supported strategies, how they work, their tim
 
 | Strategy                              | What It Does                                                                                   | Time Complexity           | When to Use                                                                                    |
 | ------------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------- | ---------------------------------------------------------------------------------------------- |
-| **MMR** (Maximal Marginal Relevance)  | Keeps the most relevant items while down-weighting those too similar to what's already picked. | **O(k · n · d)**          | Simple baseline. Fast, easy to implement, competitive results. Recommended `diversity`: 0.6–0.7 |
-| **MSD** (Max Sum of Distances)        | Prefers items that are both relevant and far from *all* previous selections.                   | **O(k · n · d)**          | Best for maximum variety (ILAD), but may sacrifice relevance. Recommended `diversity`: 0.4–0.5 |
-| **DPP** (Determinantal Point Process) | Samples diverse yet relevant items using probabilistic "repulsion."                            | **O(k · n · d + n · k²)** | **Recommended default.** Best overall in benchmarks: highest accuracy and diversity. Recommended `diversity`: 0.6–0.8 |
+| **MMR** (Maximal Marginal Relevance)  | Keeps the most relevant items while down-weighting those too similar to what's already picked. | **O(k · n · d)**          | Simple baseline. Fast, easy to implement, competitive results. Recommended `diversity`: 0.4–0.7 |
+| **MSD** (Max Sum of Distances)        | Prefers items that are both relevant and far from *all* previous selections.                   | **O(k · n · d)**          | Best for maximum variety (ILAD), but may sacrifice relevance. Recommended `diversity`: 0.3–0.5 |
+| **DPP** (Determinantal Point Process) | Samples diverse yet relevant items using probabilistic "repulsion."                            | **O(k · n · d + n · k²)** | **Recommended default.** Best overall in benchmarks: highest accuracy and diversity. Recommended `diversity`: 0.5–0.8 |
 | **COVER** (Facility-Location)         | Ensures selected items collectively represent the full dataset's structure.                    | **O(k · n²)**             | Great for topic coverage or clustering scenarios, but slower for large `n`. |
-| **SSD** (Sliding Spectrum Decomposition) | Sequence‑aware diversification: rewards novelty relative to recently shown items.     | **O(k · n · d)**          | Great for content feeds & infinite scroll where users consume sequentially. Recommended `diversity`: 0.8–0.9 |
+| **SSD** (Sliding Spectrum Decomposition) | Sequence‑aware diversification: rewards novelty relative to recently shown items.     | **O(k · n · d)**          | Great for content feeds & infinite scroll where users consume sequentially. Recommended `diversity`: 0.6–0.9 |
 
 ## Benchmarks
 
 All strategies are evaluated across 4 recommendation datasets. Full methodology and detailed results can be found in [`benchmarks`](benchmarks/).
 
-**TL;DR: Use DPP with `diversity=0.6-0.8`** for best overall diversity gains with minimal relevance loss.
+DPP is the clear winner: it boosts ILAD/ILMD by 26-44% and 54-86% respectivelywhile improving relevance by 1.8-3.1%. Use `diversity=0.5-0.8` for best overall results.
 
 ## Motivation
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 76dff7c..f326d80 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -8,8 +8,6 @@ reranked with each diversification strategy. Relevance (nDCG) and diversity (ILA
 to answer: *(1) how much can diversification improve relevance? (2) what's the relevance-diversity tradeoff?*
 Latency is also measured as the number of candidates scales.
 
-**TL;DR: DPP wins on all metrics**: best accuracy and best diversity at both relevance floors.
-
 > **Note:** COVER (coverage-based diversification) is not included because it optimizes a different
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard
 > collaborative filtering datasets.

From dc6a4996be13537e1cfda618f9a7572524263a45 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Mon, 26 Jan 2026 20:32:28 +0100
Subject: [PATCH 61/69] Updated readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0f7edd4..0bf15f0 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ The following table describes the supported strategies, how they work, their tim
 
 All strategies are evaluated across 4 recommendation datasets. Full methodology and detailed results can be found in [`benchmarks`](benchmarks/).
 
-DPP is the clear winner: it boosts ILAD/ILMD by 26-44% and 54-86% respectivelywhile improving relevance by 1.8-3.1%. Use `diversity=0.5-0.8` for best overall results.
+DPP is the clear winner: it boosts ILAD/ILMD by 26-44% and 54-86% respectively while improving relevance by 1.8-3.1%. Use `diversity=0.5-0.8` for best overall results.
 
 ## Motivation
 

From f6d37a664aa61eb87619e5e0c4bab68f36e0e878 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:35:55 +0100
Subject: [PATCH 62/69] Updated readme

---
 README.md | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 0bf15f0..ccf76c3 100644
--- a/README.md
+++ b/README.md
@@ -67,7 +67,7 @@ The `diversity` parameter tunes the trade-off between relevance and diversity: 0
 
 ## Supported Strategies
 
-The following table describes the supported strategies, how they work, their time complexity, and when to use them. The papers linked in the [references](#references) section provide more in-depth information on the strengths/weaknesses of the supported strategies.
+The papers linked in [references](#references) provide more details on each strategy.
 
 | Strategy                              | What It Does                                                                                   | Time Complexity           | When to Use                                                                                    |
 | ------------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------- | ---------------------------------------------------------------------------------------------- |
@@ -87,10 +87,10 @@ DPP is the clear winner: it boosts ILAD/ILMD by 26-44% and 54-86% respectively w
 
 Traditional retrieval systems rank results purely by relevance (how closely each item matches the query). While effective, this can lead to redundancy: top results often look nearly identical, which can create a poor user experience.
 
-Diversification techniques like MMR, MSD, COVER, and DPP help balance relevance and variety.
+Diversification techniques like MMR, MSD, COVER, DPP, and SSD help balance relevance and variety.
 Each new item is chosen not only because it’s relevant, but also because it adds new information that wasn’t already covered by earlier results.
 
-This improves exploration, user satisfaction, and coverage across many domains, for example:
+This improves exploration, user satisfaction, and coverage across many domains:
 
 - E-commerce: Show different product styles, not multiple copies of the same product.
 - News search: Highlight articles from different outlets or viewpoints.
@@ -100,12 +100,9 @@ This improves exploration, user satisfaction, and coverage across many domains,
 
 ## Examples
 
-The following examples illustrate how to apply different diversification strategies in various scenarios.
-
 <details> <summary><b>Product / Web Search</b> — Simple diversification with MMR or DPP</summary> <br>
 
-MMR and DPP are great general-purpose diversification strategies. They are fast, easy to use, and work well in many scenarios.
-For example, in a product search setting where you want to show diverse items to a user, you can diversify the top results as follows:
+MMR and DPP work well as general-purpose diversifiers. In product search, use them to avoid showing near-duplicate results:
 
 ```python
 from pyversity import diversify, Strategy
@@ -126,7 +123,7 @@ result = diversify(
 
 <details> <summary><b>Literature Search </b> — Represent the full topic space with COVER</summary> <br>
 
-COVER (Facility-Location) is well-suited for scenarios where you want to ensure that the selected items collectively represent the entire dataset’s structure. For instance, when searching for academic papers on a broad topic, you might want to cover various subfields and methodologies:
+COVER ensures the selected items collectively represent the full topic space. For academic papers, this means covering different subfields and methodologies:
 
 ```python
 from pyversity import diversify, Strategy
@@ -149,7 +146,7 @@ result = diversify(
 <summary><b>Conversational RAG</b> — Avoid redundant chunks with SSD</summary>
 <br>
 
-In retrieval-augmented generation (RAG) for conversational AI, it’s crucial to avoid feeding the model redundant or similar chunks of information within the recent conversation context. The SSD (Sliding Spectrum Decomposition) strategy is designed for sequence-aware diversification, making it ideal for this use case:
+In conversational RAG, you want to avoid feeding the model redundant chunks. SSD diversifies relative to recent context, making it a natural fit:
 
 ```python
 import numpy as np
@@ -177,7 +174,7 @@ recent_chunk_embeddings = np.vstack([recent_chunk_embeddings, chunk_embeddings[r
 
 <details> <summary><b>Infinite Scroll / Recommendation Feed</b> — Sequence-aware novelty with SSD</summary> <br>
 
-In content feeds or infinite scroll scenarios, users consume items sequentially. To keep the experience engaging, it’s important to introduce novelty relative to recently shown items. The SSD strategy is well-suited for this:
+In content feeds, users consume items sequentially. SSD introduces novelty relative to recently shown items, keeping the experience fresh:
 
 ```python
 import numpy as np
@@ -205,7 +202,7 @@ recent_feed_embeddings = np.vstack([recent_feed_embeddings, feed_embeddings[resu
 
 <details> <summary><b>Single Long Document</b> — Pick diverse sections with MSD</summary> <br>
 
-When summarizing or extracting information from a single long document, it’s beneficial to select sections that are both relevant and cover different parts of the document. The MSD strategy helps achieve this by preferring items that are far apart from each other:
+When extracting from a long document, you want sections that cover different parts. MSD prefers items that are far apart from each other:
 
 ```python
 from pyversity import diversify, Strategy

From 3119329e51a39db86a82f8298138c7d2fcaed0f9 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:39:32 +0100
Subject: [PATCH 63/69] Updated readme

---
 benchmarks/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index f326d80..51737b2 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -6,7 +6,8 @@ Each dataset uses a leave-one-out evaluation: one item per user is held out as t
 are trained on the remaining interactions (preventing leakage), candidate items are generated, then
 reranked with each diversification strategy. Relevance (nDCG) and diversity (ILAD, ILMD) are measured
 to answer: *(1) how much can diversification improve relevance? (2) what's the relevance-diversity tradeoff?*
-Latency is also measured as the number of candidates scales.
+
+Latency scaling is also measured for each strategy.
 
 > **Note:** COVER (coverage-based diversification) is not included because it optimizes a different
 > objective (topic/category coverage) and requires explicit item taxonomies not available in standard

From 6916f9f69e0d0740f7a23a03906fa92217ab2ce6 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:47:38 +0100
Subject: [PATCH 64/69] Updated settings

---
 benchmarks/README.md      | 4 ++--
 benchmarks/__main__.py    | 4 ++--
 benchmarks/core/config.py | 2 +-
 benchmarks/core/data.py   | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 51737b2..07dd171 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -291,8 +291,8 @@ from benchmarks import BenchmarkConfig, run_benchmark
 from pyversity import Strategy
 
 config = BenchmarkConfig(
-    dataset_path="local/data/ml-32m",
-    sample_users=1000,
+    dataset_path="benchmarks/data/ml-32m",
+    sample_users=2000,
     strategies=[Strategy.MMR, Strategy.DPP, Strategy.MSD, Strategy.SSD],
     diversity_values=[0.0, 0.3, 0.5, 0.7, 1.0],
 )
diff --git a/benchmarks/__main__.py b/benchmarks/__main__.py
index a163be0..7f32662 100644
--- a/benchmarks/__main__.py
+++ b/benchmarks/__main__.py
@@ -24,7 +24,7 @@
 logger = logging.getLogger(__name__)
 
 RESULTS_DIR = Path("benchmarks/results")
-DATA_DIR = Path("local/data")
+DATA_DIR = Path("benchmarks/data")
 
 
 def cmd_download() -> None:
@@ -78,7 +78,7 @@ def cmd_download() -> None:
     logger.info("Download complete")
 
 
-def cmd_run(n_runs: int = 5, overwrite: bool = False) -> None:
+def cmd_run(n_runs: int = 10, overwrite: bool = False) -> None:
     """Run benchmarks on all datasets."""
     logger.info(f"Running benchmark suite ({n_runs} runs per dataset for robustness)...")
 
diff --git a/benchmarks/core/config.py b/benchmarks/core/config.py
index 2633a5d..369dfe9 100644
--- a/benchmarks/core/config.py
+++ b/benchmarks/core/config.py
@@ -37,7 +37,7 @@ class BenchmarkConfig:
 
     # Reproducibility
     seed: int = 42
-    n_runs: int = 1  # Number of runs with different seeds for robustness
+    n_runs: int = 10  # Number of runs with different seeds for robustness
 
     def __post_init__(self) -> None:
         """Post-initialization processing."""
diff --git a/benchmarks/core/data.py b/benchmarks/core/data.py
index e8bcb83..07f7737 100644
--- a/benchmarks/core/data.py
+++ b/benchmarks/core/data.py
@@ -36,14 +36,14 @@ class DatasetInfo:
 DATASET_REGISTRY: dict[str, DatasetInfo] = {
     "ml-32m": DatasetInfo(
         name="ml-32m",
-        path="local/data/ml-32m",
+        path="benchmarks/data/ml-32m",
         dataset_type=DatasetType.MOVIELENS,
         rating_threshold=4.0,
         download_url="https://files.grouplens.org/datasets/movielens/ml-32m.zip",
     ),
     "lastfm": DatasetInfo(
         name="lastfm",
-        path="local/data/lastfm",
+        path="benchmarks/data/lastfm",
         dataset_type=DatasetType.LASTFM,
         rating_threshold=1.0,
         download_url="http://files.grouplens.org/datasets/hetrec2011/hetrec2011-lastfm-2k.zip",

From d7d89c1bd0d69cbbd30be46243a683c3c73a90aa Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:52:21 +0100
Subject: [PATCH 65/69] Updated readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ccf76c3..2ac6c02 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@
 Pyversity is a fast, lightweight library for diversifying retrieval results.
 Retrieval systems often return highly similar items. Pyversity efficiently re-ranks these results to encourage diversity, surfacing items that remain relevant but less redundant.
 
-It implements several popular diversification strategies such as MMR, MSD, DPP, SSD, and Cover with a clear, unified API. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
+It implements several popular diversification strategies such as MMR, MSD, DPP, SSD, and Cover with a clear, unified API and [benchmarks](#benchmarks) for each strategy. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
 
 
 ## Quickstart

From fe825bfc6dd46ccfb812c35281ca46a8e8aab9e2 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:53:05 +0100
Subject: [PATCH 66/69] Updated readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2ac6c02..273b129 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@
 Pyversity is a fast, lightweight library for diversifying retrieval results.
 Retrieval systems often return highly similar items. Pyversity efficiently re-ranks these results to encourage diversity, surfacing items that remain relevant but less redundant.
 
-It implements several popular diversification strategies such as MMR, MSD, DPP, SSD, and Cover with a clear, unified API and [benchmarks](#benchmarks) for each strategy. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
+It implements several popular diversification strategies such as MMR, MSD, DPP, SSD, and Cover with a clear, unified API and [benchmarks](benchmarks/) for each strategy. More information about the supported strategies can be found in the [supported strategies section](#supported-strategies). The only dependency is NumPy, making the package very lightweight.
 
 
 ## Quickstart

From 4741fdf513bd03d80e7603dc24db1b819582bba4 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:55:39 +0100
Subject: [PATCH 67/69] Updated docstrings

---
 src/pyversity/strategies/cover.py | 2 +-
 src/pyversity/strategies/dpp.py   | 3 +--
 src/pyversity/strategies/ssd.py   | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/pyversity/strategies/cover.py b/src/pyversity/strategies/cover.py
index 75412bd..b106a3f 100644
--- a/src/pyversity/strategies/cover.py
+++ b/src/pyversity/strategies/cover.py
@@ -63,8 +63,8 @@ def cover(
         # Normalize feature vectors to unit length for cosine similarity
         feature_matrix = normalize_rows(feature_matrix)
 
+    # Pure relevance: select top-k by relevance scores
     if float(theta) == 1.0:
-        # Pure relevance: select top-k by relevance scores
         topk = np.argsort(-relevance_scores)[:top_k].astype(np.int32)
         gains = relevance_scores[topk].astype(np.float32, copy=False)
         return DiversificationResult(
diff --git a/src/pyversity/strategies/dpp.py b/src/pyversity/strategies/dpp.py
index e59ccf6..fa62973 100644
--- a/src/pyversity/strategies/dpp.py
+++ b/src/pyversity/strategies/dpp.py
@@ -57,9 +57,8 @@ def dpp(
     # Normalize feature vectors to unit length for cosine similarity
     feature_matrix = normalize_rows(feature_matrix)
 
-    # Fast path for pure relevance (diversity = 0)
+    # Pure relevance: select top-k by relevance scores
     if float(diversity) == 0.0:
-        # Pure relevance: select top-k by relevance scores
         topk = np.argsort(-relevance_scores)[:top_k].astype(np.int32)
         gains = relevance_scores[topk].astype(np.float32, copy=False)
         return DiversificationResult(
diff --git a/src/pyversity/strategies/ssd.py b/src/pyversity/strategies/ssd.py
index 5d4ba5d..2910e26 100644
--- a/src/pyversity/strategies/ssd.py
+++ b/src/pyversity/strategies/ssd.py
@@ -79,7 +79,7 @@ def ssd(  # noqa: C901
     # Determine effective window size
     window_size = (n_recent + top_k) if window is None else int(window)
 
-    # Pure relevance: select top-k by raw scores
+    # Pure relevance: select top-k by relevance scores
     if float(theta) == 1.0:
         topk = np.argsort(-relevance_scores)[:top_k].astype(np.int32)
         selection_scores = relevance_scores[topk].astype(np.float32, copy=False)

From 9af941bef739f554d373b348bbd81bb3510f631e Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 08:56:59 +0100
Subject: [PATCH 68/69] Updated docstrings

---
 src/pyversity/strategies/dpp.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/pyversity/strategies/dpp.py b/src/pyversity/strategies/dpp.py
index fa62973..c92de4f 100644
--- a/src/pyversity/strategies/dpp.py
+++ b/src/pyversity/strategies/dpp.py
@@ -24,7 +24,8 @@ def dpp(
 
     This strategy selects a diverse and relevant subset of `k` items by
     maximizing the determinant of a kernel matrix that balances item relevance
-    and pairwise similarity. Note that
+    and pairwise similarity. It uses a greedy algorithm to iteratively select items
+    that contribute the most to the overall diversity and relevance of the set.
 
     :param embeddings: 2D array of shape (n_samples, n_features).
     :param scores: 1D array of relevance scores for each item.

From 2b13487ae85fc2b5bdc457abd21fad85249a2883 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 27 Jan 2026 09:02:17 +0100
Subject: [PATCH 69/69] Pinned dependencies for benchmarks

---
 pyproject.toml | 24 +++++++++---------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a6c0068..a587a92 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,14 +46,15 @@ dev = [
     "types-requests",
 ]
 benchmarks = [
-    "datasets",
-    "matplotlib",
-    "pandas",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "seaborn",
-    "tqdm",
+    "datasets~=3.6.0",
+    "huggingface-hub~=0.30.0",
+    "matplotlib~=3.10.0",
+    "pandas~=2.3.0",
+    "requests~=2.32.0",
+    "scikit-learn~=1.8.0",
+    "scipy~=1.16.0",
+    "seaborn~=0.13.0",
+    "tqdm~=4.67.0",
 ]
 
 [tool.ruff]
@@ -113,10 +114,3 @@ pyversity = ["py.typed"]
 
 [tool.setuptools.dynamic]
 version = {attr = "pyversity.version.__version__"}
-
-[dependency-groups]
-dev = [
-    "datasets>=4.5.0",
-    "huggingface-hub>=1.3.2",
-    "tqdm>=4.67.1",
-]