Generative Recommendation

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Overview

Generative recommendation studies how to produce personalized items or ranked lists directly from user histories and context, instead of scoring a fixed candidate set. Common approaches encode items as semantic IDs and train autoregressive Transformers or language models, while diffusion, flow-based generation, and continuous retrieval offer alternatives. Training combines next-item supervision with semantic or behavioral signals, and sometimes reinforcement learning; evaluation uses sequential benchmarks such as Amazon datasets alongside production A/B tests. A central challenge is balancing useful item representations and reachability—including for cold or long-tail items—with accuracy, fairness, and the latency demands of large-scale serving.

Latest papers

CardsList

107 papers11 papers in the last four weeks, down 15% on the four weeks before. 0.1% of all new papers.

  1. Deploying Semantic ID-based Generative Retrieval for Large-Scale Podcast Discovery at Spotify

    Mar 18, 2026Edoardo D'Amico, Marco De Nadai, Praveen Chandar +41Generative RecommendationLarge Language Model-Based Recommendation

  2. GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items

    Mar 15, 2026Chenglei Shen, Teng Shi, Weijie Yu +2Generative RecommendationCold-Start Recommendation

    Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · School of Information Technology and Management, University of International Business and Economics, Beijing, China

  3. Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs

    Mar 14, 2026Xiaofei Zhu, Jinfei Chen, Feiyang Yuan +1Recommender SystemsGenerative Recommendation

    Chongqing University of Technology, College of Computer Science and Engineering, Chongqing, China · Chongqing Normal University, College of Computer and Information Science, Chongqing, China

  4. Beyond Interleaving: Causal Attention Reformulations for Generative Recommender Systems

    Mar 11, 2026Hailing ChengCausal AttentionGenerative Recommendation

    Linkedin Inc Mountain View, California, USA

  5. Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF

    Mar 10, 2026Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2Recommender SystemsGenerative Recommendation

    Department of Management Science & Engineering, Stanford University, CA, USA · Meta, CA, USA · Netflix Research, CA, USA

  6. Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

    Mar 3, 2026Yangchen Zeng, Jinze WangRecommender SystemsSemantic ID Recommendation

  7. MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation

    Feb 28, 2026Rong Shan, Aofan Yu, Bo Chen +7Recommender SystemsMuon Optimizer

    Shanghai Jiao Tong University, Shanghai, China · Kuaishou Technology, Beijing, China

  8. SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress

    Feb 26, 2026Yang Yu, Lei Kou, Huaikuan Yi +6Recommender SystemsGenerative Recommendation

    Alibaba International Digital Commercial Group, Hangzhou, China

  9. Generative Recommendation for Large-Scale Advertising

    Feb 26, 2026Ben Xue, Dan Liu, Lixiang Wang +27RL for RecommendationRecommender Systems

    Kuaishou Technology, Beijing, China

  10. U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation

    Feb 26, 2026Zezheng Wu, Rui Wang, Xinghe Cheng +4Machine UnlearningLLM Unlearning

    Guilin University of Electronic Technology, Guilin, China · University of Southampton, Southampton, United Kingdom · Jinan University, Guangzhou, China +1

  11. Trie-Aware Transformers for Generative Recommendation

    Feb 25, 2026Zhenxiang Xu, Jiawei Chen, Sirui Chen +5Generative RecommendationSequential Recommendation

    State Key Laboratory of Blockchain and Data Security, Zhejiang University. · College of Computer Science and Technology, Zhejiang University. · Zhejiang University Hangzhou, China +2

  12. T-REX: Transformer-Based Category Sequence Generation for Grocery Basket Recommendation

    Feb 23, 2026Soroush Mokhtari, Muhammad Tayyab Asif, Sergiy ZubatiyRecommender SystemsSequential Recommendation

    Amazon WorldWide Grocery · Fulfilled By Amazon

  13. DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation

    Feb 21, 2026Yangchen Zeng, Zhenyu Yu, Zhiyuan Hu +3Recommender SystemsGenerative Recommendation

    Southeast University

  14. NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines

    Feb 20, 2026Junwei Wu, Runze Yan, Hanqi Luo +7Multi-Agent LLM SystemsClinical Decision Support

    Emory University, Atlanta, GA, USA · Texas Woman’s University, Denton, TX, USA · Cecelia Health, New York, NY, USA

  15. Variable-Length Semantic IDs for Recommender Systems

    Feb 18, 2026Kirill KhrylchenkoSemantic ID RecommendationGenerative Recommendation

    HSE University, Moscow, Russia

  16. xGR: Efficient Generative Recommendation Serving at Scale

    Dec 12, 2025Qingxiao Sun, Tongxuan Liu, Shen Zhang +13LLM ServingLLM Inference Acceleration

    Beihang University · JD Company · University of Science and Technology Beijing +1

  17. CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

    Nov 27, 2025Tianxin Wei, Xuying Ning, Xuxing Chen +6Sequential RecommendationHierarchical Representation Learning

    University of Illinois Urbana-Champaign · Meta · University of California San Diego