Large Language Model-Based Recommendation

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Overview

The field studies how language models can use item descriptions, interaction histories, and conversational feedback to predict and explain what people may want. Common approaches include instruction-tuned or reinforcement-trained recommenders, retrieval-augmented generation, semantic item IDs, and hybrids that combine language representations with collaborative filtering or graph signals. Work spans sequential and conversational recommendation, cold-start retrieval, and large-scale search and feeds, with distillation and offline feature generation used to reduce serving costs. A central tension is that semantic reasoning and fluent explanations do not guarantee accurate, grounded recommendations: candidate retrieval, user-data alignment, bias, safety, and latency remain important challenges.

Latest papers

CardsList

213 papers18 papers in the last four weeks, down 18% on the four weeks before. 0.1% of all new papers.

  1. Category-Anchored Large Language Models for Next POI Recommendation

    Oct 29, 2026Lexi Bai, Xiaopeng Zeng, Xun Zhou +2Sequential RecommendationLarge Language Model-Based Recommendation

    Harbin Institute of Technology, Shenzhen, China · City University of Hong Kong, Hong Kong SAR, China.

  2. Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation

    Oct 27, 2026Yunqin Zhu, Yao XieLarge Language Model-Based RecommendationLLM Fine-Tuning

    Georgia Institute of Technology, Atlanta, GA, USA

  3. Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence

    Oct 27, 2026Carlos Peláez-González, Francisco Herrera, Andrés Herrera-PoyatosRecommender SystemsLarge Language Model-Based Recommendation

    Department of Computer Science and Artificial Intelligence, Andalusian Institute of Data Science and Computational Intelligence (DaSCI), University of Granada, Spain. · Department of Algebra, University of Granada, Spain.

  4. RPSD: Retrieval-Privileged On-Policy Self-Distillation with Optimal Transport

    Oct 27, 2026Dongyue Li, Zebang Cheng, Zhongyi Deng +3Sequential RecommendationRL for Recommendation

    Nagoya University · Shenzhen University · Sichuan Agricultural University +2

  5. Modeling Preference Dependencies with Directed Acyclic Graphs for Narrative-Driven Recommendation

    Oct 27, 2026Mingzhe Lu, Jian Qin, Ruize Qin +3Recommender SystemsPreference Modeling

    Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · Faculty of Engineering and IT, The University of Melbourne, Melbourne, Australia +1

  6. JevRec: Linguistic Encoding with Jev-like Decision Modeling for LLM-based Generative Recommendation

    Oct 27, 2026Qiyong Zhong, Jiajie Su, Ming Yang +1Generative RecommendationLarge Language Model-Based Recommendation

    Zhejiang University, Hangzhou, China

  7. CRIM: Composable and Reusable Item Memory for Efficient Personalized Retrieval

    Oct 26, 2026Haolin Li, Haipeng Zhang, Mang Li +5Information RetrievalMemory-Augmented Language Models

    Tsinghua University, Beijing, China · Alibaba International Digital Commerce Group, Beijing, China

  8. LLM Multi-Agent Systems Are Vulnerable to Herding and Runaway Cascades

    Oct 22, 2026Farnoosh Hashemi, Shiyu Ji, Joice Chen +1Multi-Agent LLM SystemsOpinion Dynamics

    Cornell University · Stanford University

  9. Where Do LLMs Learn to Recommend AI?

    Oct 21, 2026Benaya Trabelsi, Jonathan Shaki, Sarit KrausLarge Language Model-Based RecommendationLLM Prompting

    Bar-Ilan University

  10. Decision4Rec: A Jev-Inspired Framework of Decision Models on Knowledge and Active Graphs for Multi-Agent Recommender Systems

    Oct 19, 2026Marco Polignano, Het Darshan Mehta, Pasquale Lops +2Recommender SystemsLarge Language Model-Based Recommendation

    Università degli studi di Bari Aldo Moro, Piazza Umberto I, 70121 Bari, Italy · Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany · Leibniz Institute for Educational Media | Georg Eckert Institute, Freisestr. 1, 38118 Braunschweig, Germany

  11. Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap

    Oct 13, 2026Renbo Tu, Yang Liu, Shuying SunPopularity Bias in Recommender SystemsGenerative Retrieval

    Shopify · University of Toronto

  12. Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators

    Oct 13, 2026Andrei Niculae, Adrian Cosma, Emilian RadoiAutomatic Prompt OptimizationLarge Language Model-Based Recommendation

    National University of Science and Technology POLITEHNICA Bucharest, Romania · IDSIA, Dalle Molle Institute for Artificial Intelligence, Lugano, Switzerland

  13. Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders

    Oct 13, 2026Xin Li, Hao Jiang, Jinghao Guo +2RL for RecommendationLarge Language Model-Based Recommendation

    Nanyang Technological University, Singapore

  14. ARMED: Adaptive Retrieval-Augmented Medication Recommendation

    Oct 12, 2026Kyungkeon Chung, Se Un Park, Hyejin Lee +1Medication RecommendationAdaptive RAG

    UX Factory, Inc., Seongnam-si, South Korea

  15. Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

    Oct 11, 2026Sejoon Oh, Fernando Amat Gil, Evan Smothers +11Large Language Model-Based RecommendationSemantic ID Recommendation

    Netflix Personalization Foundations Team

  16. Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation

    Oct 8, 2026Leikun Liang, Guoshuai Wang, Xingsheng He +4Recommender SystemsLarge Language Model-Based Recommendation

    Alibaba Group Hangzhou, China

  17. Multimodal Graph Retrieval-Augmented Sequential Recommendation via Collaborative Filtering Paths

    Oct 8, 2026Jason Marcell Setiadi, Xin Cao, Lina YaoRecommender SystemsSequential Recommendation

    University of New South Wales, Sydney, Australia

  18. Personal-Agent Mediated Recommendation with Cross-Platform User History

    Oct 6, 2026Yu Xia, Jiangfan Zhang, Jun Xiao +2LLM PersonalizationRecommender Systems

    University of California San Diego · Meta · Meta AI

  19. Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation

    Oct 2, 2026Yu Hou, Nathaniel Kang, Pengkai Wang +1Large Language Model-Based RecommendationExplainable Recommendation

    School of Mathematics and Computing (Computational Science and Engineering), Yonsei University Seoul, Republic of Korea · School of Computer Science and Engineering, Kyungpook National University Daegu, Republic of Korea · Department of Electrical and Computer Engineering, Sungkyunkwan University Suwon, Republic of Korea +1

  20. RPTune: Learned Context Curation for LLM Catalog Search

    Oct 1, 2026Chuxuan Hu, Hejie Cui, Norman Huang +3In-Context RetrievalLLM Fine-Tuning

    Google · University of Illinois Urbana-Champaign

  21. Decision-Oriented Recommendation Reranking: An Empirical Study of Its Promise and Limitations

    Sep 30, 2026Hanjia Lyu, Yinglong XiaLLM RerankingLarge Language Model-Based Recommendation

    Singapore Management University · Meta AI

  22. VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

    Sep 29, 2026Yurong Hao, Wen Zhou, Guowei Guan +3Adversarial AttacksAgent Memory Poisoning

    College of Computing and Data Science, Nanyang Technological University

  23. ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

    Sep 29, 2026Haohao Qu, Yongcheng Jing, Chun Hin Chan +3Long-Context ModelingRecommender Systems

    The Hong Kong Polytechnic University Hong Kong · Nanyang Technological University Singapore

  24. Selective Elicitation as a Commercial Influence Channel: A Reproducible Synthetic Shopping-Agent Stress Test

    Sep 29, 2026Jiapeng LiPreference ElicitationLarge Language Model-Based Recommendation

  25. Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale

    Sep 28, 2026Ghazal Fazelnia, Paul Gigioli, Eliza Klyce +16Recommender SystemsMusic Recommendation

    Spotify, USA, UK, Sweden

  26. Do Evidence-Reading Diagnostics Improve Interface Selection in Small LLM Recommenders?

    Sep 25, 2026Han Chen, Yingrui LiLarge Language Model-Based Recommendation

  27. AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

    Sep 25, 2026Ryoma SatoRecommender SystemsLarge Language Model-Based Recommendation

    National Institute of Informatics

  28. KuaFu: Compressing Long User Behavior into Understanding at Billion Scale

    Sep 25, 2026Jiahao Hui, Lin Zhu, Ben Tan +7User Preference ModelingLLM Compression

    Tencent Inc., Shenzhen, China

  29. Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery

    Sep 20, 2026Xiao Liu, Yanwei Song, Srivaths Ranganathan +7Music RecommendationRecommender Systems

    Google LLC USA

  30. Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles

    Sep 17, 2026Noah Mamié, Laurin van den BerghRecommender SystemsLarge Language Model-Based Recommendation

    University of Zurich Department of Informatics