Awesome Search - this is all about the (e-commerce, but not only) search and its awesomeness

Systematic Review Query Visualisation and Understanding Interface

IntentsKB: A Knowledge Base of Entity-Oriented Search Intents - CIKM'18

Towards an Understanding of Entity-Oriented Search Intents - ECIR'18

Presentation and Code for talk at Conferences - MLDS-2020 and DHS-2019

Target Type Identification for Entity-Bearing Queries - SIGIR'17

--------------[deprecated]-------------- Modern search engine focused on fast, structured, and AI-powered knowledge discovery. I desperately need funding, please contact me on Instagram.

模块化、可组合的 RAG 检索库 —— 像锻造零件一样构建你的检索管道。Modular, composable RAG retrieval library —— Build your retrieval pipeline like forging parts.

AI language system for understanding queries, summarizing info, and giving clear answers.

A new package that processes user queries about why small voting or ranking projects get flagged as spam so easily. It uses natural language processing to understand the input and generates a structur

QPP for Clarification Need Prediction in context-grounded multi-turn Conversation. Clean implementations of QPP baselines suitable for multi-turn conversational dataset with ranked documents (opt.). Designed to detect ambiguous search queries.

A reference implementation of modern search architecture that prioritizes deterministic intent enforcement (BM25 + boosts), bounded semantic expansion, and explainable ranking. Built to reflect real production search systems rather than end-to-end black-box ML.

A robust Retrieval-Augmented Generation (RAG) system for noisy, multi-intent queries using LLM-based query understanding. Implemented in Python with PostgreSQL and OpenSearch for retrieval and storage.

🔍 Process user queries about spam flags on small voting projects with natural language insights and structured responses.