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<!DOCTYPE html>
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<meta name="description" content="KDD 2026 Tutorial: Generative Recommendation — Foundations and Frontiers. A tri-decoupled perspective on tokenization, architecture, and optimization.">
<meta name="keywords" content="Generative Recommendation, KDD 2026, Tutorial, Tokenization, Semantic ID, LLM, Recommender Systems, Kuaishou, City University of Hong Kong">
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<title>Tutorial on Generative Recommendation: Foundations and Frontiers — KDD 2026</title>
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<a class="navbar-item" href="#abstract">Abstract</a>
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<div class="is-size-6 publication-venue">KDD 2026 · Half-Day Tutorial · Jeju Island, Republic of Korea</div>
<h1 class="title is-1 publication-title">
Tutorial on Generative Recommendation:<br/>Foundations and Frontiers
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">Xiaopeng Li<sup>1</sup>,</span>
<span class="author-block">Yejing Wang<sup>1</sup>,</span>
<span class="author-block">Honghui Bao<sup>2</sup>,</span>
<span class="author-block">Bo Chen<sup>2</sup>,</span>
<span class="author-block">Kuo Cai<sup>2</sup>,</span>
<span class="author-block">Wenlin Zhang<sup>1</sup>,</span>
<span class="author-block">Ziwei Liu<sup>1</sup>,</span>
<span class="author-block">Sheng Zhang<sup>1</sup>,</span>
<span class="author-block">Binhao Wang<sup>1</sup>,</span>
<span class="author-block">Qinglin Jia<sup>2</sup>,</span>
<span class="author-block">Qiang Luo<sup>2</sup>,</span>
<span class="author-block">Ruiming Tang<sup>2</sup>,</span>
<span class="author-block"><a href="https://zhaoxyai.github.io/">Xiangyu Zhao</a><sup>1</sup></span>
</div>
<div class="is-size-6 publication-affiliations">
<span><sup>1</sup> City University of Hong Kong</span>
·
<span><sup>2</sup> Kuaishou Technology</span>
</div>
<div class="publication-meta">
<span class="tag is-light"><i class="fas fa-calendar"></i> August 09–13, 2026</span>
<span class="tag is-light"><i class="fas fa-clock"></i> Half-Day (3 hours)</span>
<span class="tag is-light"><i class="fas fa-map-marker-alt"></i> Jeju Island, Korea</span>
</div>
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<span class="link-block">
<a class="external-link button is-small is-rounded is-link"
href="https://doi.org/10.1145/3770855.3816450">
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<span>Paper</span>
</a>
</span>
<span class="link-block">
<a class="external-link button is-small is-rounded is-link"
href="https://www.preprints.org/manuscript/202512.0203">
<span class="icon"><i class="fas fa-book-open"></i></span>
<span>Survey</span>
</a>
</span>
<span class="link-block">
<a class="external-link button is-small is-rounded is-link"
href="https://github.com/Kuaishou-RecModel/Tri-Decoupled-GenRec">
<span class="icon"><i class="fab fa-github"></i></span>
<span>Paper List</span>
</a>
</span>
<span class="link-block">
<a class="external-link button is-small is-rounded is-link"
href="#schedule">
<span class="icon"><i class="fas fa-list-check"></i></span>
<span>Schedule</span>
</a>
</span>
<span class="link-block">
<a class="external-link button is-small is-rounded is-link is-disabled"
href="#">
<span class="icon"><i class="fas fa-display"></i></span>
<span>Slides (TBA)</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="abstract" class="section">
<div class="container">
<div class="content">
<div class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Abstract</h2>
<p>
In the current digital ecosystem, recommender systems serve as the core infrastructure for navigating large-scale
content catalogs and delivering personalized services, typically following multi-stage discriminative pipelines
(e.g., retrieval, ranking, and re-ranking). However, their fragmented architectures cause cascading cross-stage
error propagation and suboptimal hardware utilization. This motivates a paradigm shift toward
<strong>Generative Recommendation (GR)</strong>. GR mitigates these issues through end-to-end unified generative
modeling, optimizing for multi-dimensional preference objectives beyond local user behaviors.
</p>
<p>
This tutorial comprehensively surveys recent generative recommendation advances through a
<strong>tri-decoupled perspective</strong> centered on <em>tokenization</em>, <em>architecture</em>, and
<em>optimization</em>—the three foundational components shaping these systems. Specifically, we summarize the
evolution of tokenization strategies, analyze the trade-offs of major generative architectures, and summarize
the transition from supervised next-token prediction to reinforcement-learning-based strategies. Connecting these
technical developments to practical deployment patterns and open challenges, we provide researchers and
practitioners a foundational reference and actionable blueprint for building next-generation generative
recommender systems.
</p>
</div>
</div>
<div id="overview" class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Framework Overview</h2>
<figure class="image figure-frame">
<img src="./static/images/framework.png" alt="Framework overview of discriminative and generative recommendation paradigms.">
</figure>
<p class="figure-caption">
<strong>Figure 1.</strong> Framework overview of discriminative (left) and generative (right)
recommendation paradigms. Generative Recommendation unifies tokenization, architecture, and optimization into
an end-to-end pipeline that directly generates item identifiers.
</p>
</div>
</div>
<div class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">What You Will Learn</h2>
<div class="columns is-multiline highlight-cards">
<div class="column is-half">
<div class="card-highlight">
<span class="icon-large"><i class="fas fa-route"></i></span>
<h3>Evolutionary Trajectory</h3>
<p>From discriminative recommendation to end-to-end generative modeling, and why this shift mitigates cascaded errors.</p>
</div>
</div>
<div class="column is-half">
<div class="card-highlight">
<span class="icon-large"><i class="fas fa-layer-group"></i></span>
<h3>Tri-Decoupled Perspective</h3>
<p>Three core dimensions: <em>Tokenization</em>, <em>Architecture</em>, and <em>Optimization</em>, each with its own evolving trends.</p>
</div>
</div>
<div class="column is-half">
<div class="card-highlight">
<span class="icon-large"><i class="fas fa-industry"></i></span>
<h3>Practical Deployments</h3>
<p>How GR systems are implemented across industrial stages (retrieval, ranking, end-to-end) and applications.</p>
</div>
</div>
<div class="column is-half">
<div class="card-highlight">
<span class="icon-large"><i class="fas fa-compass"></i></span>
<h3>Open Challenges</h3>
<p>Key bottlenecks and research questions, from efficiency and reasoning to interactive agents and pure content generation.</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="audience" class="section section-light">
<div class="container">
<div class="content">
<div class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Target Audience & Prerequisites</h2>
<p>
This tutorial is designed for researchers, practitioners, and students interested in modern recommender
systems, especially the transition from discriminative ranking pipelines to unified Generative Recommendation
(GR) frameworks. The material is organized progressively, making it accessible to advanced students while
still providing a systematic view of the design space of GR.
</p>
<div class="columns">
<div class="column">
<h3 class="title is-4"><i class="fas fa-check-circle"></i> Expected Background</h3>
<ul>
<li>Basic understanding of machine learning and recommender systems</li>
<li>Common concepts in sequence modeling</li>
</ul>
</div>
<div class="column">
<h3 class="title is-4"><i class="fas fa-star"></i> Helpful (but Not Required)</h3>
<ul>
<li>Familiarity with Transformer-based models</li>
<li>Item tokenization and semantic identifiers</li>
<li>Generative training objectives (next-token prediction, preference alignment)</li>
</ul>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="schedule" class="section">
<div class="container">
<div class="content">
<div class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Tutorial Schedule</h2>
<p>
The tutorial is presented as a <strong>half-day (3-hour) lecture</strong> with slides and handouts.
The full schedule is organized as follows:
</p>
<div class="schedule">
<!-- Item 1 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">20 min</span>
<span class="schedule-slot">Session 1</span>
</div>
<div class="schedule-detail">
<h3>Background and Preliminaries</h3>
<ul>
<li>Discriminative Recommendation</li>
<li>Generative Recommendation</li>
<li>Organization of the Tutorial</li>
</ul>
</div>
</div>
<!-- Item 2 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">35 min</span>
<span class="schedule-slot">Session 2</span>
</div>
<div class="schedule-detail">
<h3>Tokenization Strategies</h3>
<ul>
<li>Sparse ID-based Identifiers</li>
<li>Text-based Identifiers</li>
<li>Semantic ID (SID)-based Identifiers and Challenges</li>
</ul>
</div>
</div>
<!-- Item 3 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">40 min</span>
<span class="schedule-slot">Session 3</span>
</div>
<div class="schedule-detail">
<h3>Model Architecture</h3>
<ul>
<li>Encoder-Decoder Architecture</li>
<li>Decoder-Only Architecture</li>
<li>Diffusion-Based Architecture</li>
</ul>
</div>
</div>
<!-- Coffee break placeholder -->
<div class="schedule-item schedule-break">
<div class="schedule-time">
<span class="schedule-duration">—</span>
<span class="schedule-slot">Break</span>
</div>
<div class="schedule-detail">
<h3><i class="fas fa-mug-hot"></i> Coffee Break <span class="tba">(time TBA)</span></h3>
</div>
</div>
<!-- Item 4 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">35 min</span>
<span class="schedule-slot">Session 4</span>
</div>
<div class="schedule-detail">
<h3>Optimization Strategies</h3>
<ul>
<li>Supervised Learning (NTP & NCE Modeling)</li>
<li>Preference Alignment (DPO & GRPO Modeling)</li>
</ul>
</div>
</div>
<!-- Item 5 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">25 min</span>
<span class="schedule-slot">Session 5</span>
</div>
<div class="schedule-detail">
<h3>Applications</h3>
<ul>
<li>GR in Cascaded Systems (Retrieval, Ranking, End-to-End)</li>
<li>Application Scenarios (Cold Start, Cross-Domain, Search, Auto-Bidding)</li>
</ul>
</div>
</div>
<!-- Item 6 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">10 min</span>
<span class="schedule-slot">Session 6</span>
</div>
<div class="schedule-detail">
<h3>Challenges and Future Directions</h3>
<ul>
<li>End-to-End Modeling and Efficiency</li>
<li>Reasoning and Data Optimization</li>
<li>Interactive Agents and the Shift to Pure Content Generation</li>
</ul>
</div>
</div>
<!-- Item 7 -->
<div class="schedule-item">
<div class="schedule-time">
<span class="schedule-duration">15 min</span>
<span class="schedule-slot">Session 7</span>
</div>
<div class="schedule-detail">
<h3>Conclusion and Q&A</h3>
</div>
</div>
</div>
<p class="tba-note">
<i class="fas fa-info-circle"></i>
Exact session date and start time will be announced once KDD 2026 releases the official program.
</p>
</div>
</div>
</div>
</div>
</section>
<!--
Presenters & Contributors section is temporarily hidden.
Uncomment when the presenter list is finalized.
<section id="presenters" class="section section-light">
<div class="container">
<div class="content">
<div class="columns is-centered">
<div class="column is-two-thirds">
<h2 class="title is-2">Presenters</h2>
<p>The tutorial is jointly presented by researchers from City University of Hong Kong and Kuaishou Technology.</p>
</div>
</div>
<div class="columns is-centered is-multiline presenters">
<div class="column is-half">
<div class="presenter-card">
<h3>Xiaopeng Li <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Ph.D. Candidate, City University of Hong Kong</div>
<p>Research on Recommendation, Information Retrieval, and Personalization RAG. 20+ papers in top venues
(TOIS, KDD, ACL, WWW, NeurIPS, CIKM) with 500+ citations. Best Student Team Award, KDD Cup 2024
(Multi-task Online Shopping Challenge for LLMs). Co-organized a WWW'25 tutorial on Joint Modeling in Recommendation.</p>
</div>
</div>
<div class="column is-half">
<div class="presenter-card">
<h3>Yejing Wang <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Ph.D. Candidate, City University of Hong Kong</div>
<p>Focuses on industrially deployable discriminative and generative recommender systems. 20+ papers
(WWW, KDD, SIGIR, ICDM) with 600+ citations. Deployed scalable systems at Taobao and Xiaohongshu serving
hundreds of millions of users. Co-organizer of tutorials at KDD'25, WWW'25, WSDM'23, and WWW'22.</p>
</div>
</div>
<div class="column is-half">
<div class="presenter-card">
<h3>Honghui Bao <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Kuaishou Technology</div>
<p>Specializes in Large Language Models, Generative Recommendation, and Heterogeneous User Modeling.
Led the globally recognized OpenOneRec project. Reviewer for SIGIR, KDD, RecSys, TOIS, and TORS. 200+ citations.</p>
</div>
</div>
<div class="column is-half">
<div class="presenter-card">
<h3>Bo Chen <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Senior Researcher, Kuaishou Technology</div>
<p>Previously at Huawei Noah's Ark Lab; M.S. from Shanghai Jiao Tong University. Research on generative
recommendation, LLM-enhanced recommendation, and computational advertising. 70+ papers in top venues;
Best Paper Award at DLP-KDD 2021.</p>
</div>
</div>
<div class="column is-half">
<div class="presenter-card">
<h3>Kuo Cai <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Senior Researcher, Kuaishou Technology</div>
<p>M.S. from Beijing University of Posts and Telecommunications. Primary contributor to Kuaishou's
OneRec, OneRec-V2, OneRec-Think, and OneMall projects. Publications at ACL, ICLR, WSDM, CIKM; 300+ citations.</p>
</div>
</div>
<div class="column is-half">
<div class="presenter-card">
<h3>Xiangyu Zhao <span class="tag is-info is-light">In-Person</span></h3>
<div class="presenter-affil">Associate Professor, City University of Hong Kong</div>
<p>Research on data mining and machine learning, particularly personalization, recommender systems,
online advertising, and search. 150+ papers in top venues. KDD 2025 Runner-Up Best Paper,
ICDM 2021/2022 Best-Ranked Papers, Global Top Chinese New Stars in AI. Organizer of multiple workshops
and tutorials at KDD, WWW, SIGIR, CIKM, RecSys.</p>
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-two-thirds">
<h3 class="title is-3">Contributors</h3>
<div class="contributors">
<div class="contributor">
<strong>Wenlin Zhang</strong> — Ph.D. candidate, CityU. LLMs, Search Agents, Recommender Systems.
</div>
<div class="contributor">
<strong>Ziwei Liu</strong> — Ph.D. student, CityU. Information Retrieval and Recommendation.
</div>
<div class="contributor">
<strong>Sheng Zhang</strong> — Ph.D. student, CityU. Sequential recommendation and agent memory.
</div>
<div class="contributor">
<strong>Binhao Wang</strong> — Ph.D. student, CityU. Recommender Systems and LLM Agents.
</div>
<div class="contributor">
<strong>Qinglin Jia</strong> — Senior Researcher, Kuaishou. Previously at Huawei Noah's Ark Lab.
</div>
<div class="contributor">
<strong>Qiang Luo</strong> — Head of Short Video Recommendation Recall and Pre-ranking, Kuaishou.
</div>
<div class="contributor">
<strong>Ruiming Tang</strong> — Senior Director, Kuaishou; Head of Recommendation Ranking Model Center.
100+ papers with 13,000+ citations; AE of TOIS and TORS.
</div>
</div>
</div>
</div>
</div>
</div>
</section>
-->
<section id="survey" class="section">
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<h2 class="title is-2">Companion Survey</h2>
<p>
This tutorial is grounded in our companion survey, which provides a comprehensive review of generative
recommendation through a <strong>tri-decoupled</strong> perspective — covering the evolution of tokenization
strategies, generative architectures, optimization objectives, as well as practical deployment patterns and
open challenges.
</p>
<article class="survey-card">
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<span class="survey-badge">Survey · 2025</span>
<h3 class="survey-title">
A Survey of Generative Recommendation from a Tri-Decoupled Perspective:<br/>
Tokenization, Architecture, and Optimization
</h3>
<p class="survey-authors">
Xiaopeng Li, Bo Chen, Junda She, Shiteng Cao, You Wang, Qinglin Jia,
Haiying He, Zheli Zhou, Zhao Liu, Ji Liu, <em>et al.</em>
</p>
<p class="survey-desc">
We organize recent generative recommendation advances along three decoupled yet integrated axes —
<em>Tokenization</em>, <em>Architecture</em>, and <em>Optimization</em> — and connect them to
industrial deployment patterns and open research challenges.
</p>
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href="https://www.preprints.org/manuscript/202512.0203">
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<span>Read on Preprints.org</span>
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href="https://github.com/Kuaishou-RecModel/Tri-Decoupled-GenRec">
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<span>Paper List</span>
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<div class="survey-pill survey-pill-1">Tokenization</div>
<div class="survey-pill survey-pill-2">Architecture</div>
<div class="survey-pill survey-pill-3">Optimization</div>
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<h2 class="title is-2">Citing This Tutorial</h2>
<p>If you find this tutorial useful, please consider citing:</p>
<pre><code class="lang-bibtex">@inproceedings{li2026genrec,
title = {Tutorial on Generative Recommendation: Foundations and Frontiers},
author = {Li, Xiaopeng and Wang, Yejing and Bao, Honghui and Chen, Bo and Cai, Kuo
and Zhang, Wenlin and Liu, Ziwei and Zhang, Sheng and Wang, Binhao
and Jia, Qinglin and Luo, Qiang and Tang, Ruiming and Zhao, Xiangyu},
booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining (KDD '26)},
year = {2026},
doi = {10.1145/3770855.3816450}
}</code></pre>
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