User Preference Modeling

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Overview

User preference modeling studies how to infer and represent people’s priorities from choices, interaction histories, ratings, and direct feedback so systems can make more relevant recommendations and decisions. Common approaches include choice and reward models, latent factors and user profiles, Bayesian questioning, and language-model memory; recommendation systems also use temporal models to track changing interests. A central challenge is separating stable preferences from short-term intent while learning from sparse, noisy, or biased behavior, often using online feedback or shared patterns across users. Benchmarks increasingly test whether systems update preferences over long interactions and apply them only in the right context, where reliable inference does not yet guarantee appropriate action.

Latest papers

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271 papers42 papers in the last four weeks, up 45% on the four weeks before. 0.2% of all new papers.

  1. Probably Approximately Consensus: On the Learning Theory of Finding Common Ground

    Apr 23, 2026Carter Blair, Ben Armstrong, Shiri Alouf-Heffetz +2User Preference ModelingPreference Learning

    University of Waterloo · Tulane University · Ben Gurion University +1

  2. Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation

    Apr 23, 2026Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8Sequential RecommendationLanguage Model Distillation

    Independent researcher, Belgrade, Serbia · ITMO University, Saint Petersburg, Russia · Innopolis University, Innopolis, Russia +3

  3. Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management

    Apr 23, 2026Yanan Wang, Yong GeLLM Hallucination MitigationLLM Fine-Tuning

    Department of Information Systems and Operations Management, The University of Texas at Arlington · Department of Management Information Systems, University of Arizona

  4. Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation

    Apr 22, 2026Dongding Lin, Jian Wang, Yongqi Li +1Recommender SystemsLarge Language Model-Based Recommendation

    Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University

  5. Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback

    Apr 22, 2026Rongtao Zhang, Xin Zhu, Masoume Pourebadi Khotbehsara +3Pairwise Preference LearningActive Preference Learning

    Department of Computer Science University of Southern California Los Angeles, California, USA

  6. PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models

    Apr 21, 2026Salvatore Greco, Jacek Karolczak, Roman Słowiński +1Explainable Artificial IntelligenceUser Preference Modeling

    University of Catania, Department of Economics and Business, Corso Italia 55, Catania, 95129, Italy · Poznan University of Technology, Institute of Computing Science,2026 Piotrowo 2, Poznań, 60-965, Poland · bPoznan University of Technology, Institute of Computing Science,2026 Piotrowo 2, Poznań, 60-965, Poland +1

  7. Personalized Benchmarking: Evaluating LLMs by Individual Preferences

    Apr 21, 2026Cristina Garbacea, Heran Wang, Chenhao TanLLM EvaluationUser Preference Modeling

    University of Chicago

  8. Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents

    Apr 20, 2026Akriti Jain, Anish Mulay, Divyansh Verma +3AI-Assisted Decision MakingHuman-AI Decision Making

    Adobe Research, India · IIT Madras · Work done during internship at Adobe Research +4

  9. Latent Preference Modeling for Multi-Session Personalized Tool Calling

    Apr 20, 2026Yejin Yoon, Minseo Kim, Taeuk KimLLM PersonalizationLLM Agent Memory

    Hanyang University, Seoul, Republic of Korea

  10. HorizonBench: Long-Horizon Personalization with Evolving Preferences

    Apr 19, 2026Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9Long-Context ModelingUser Preference Modeling

    University of Washington · Meta · OpenAI +1

  11. From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

    Apr 18, 2026Abhirup Dasgupta, Fernando Spadea, Oshani SeneviratneKG ConstructionKnowledge Representation

    Rensselaer Polytechnic Institute, Troy NY 12180, USA

  12. Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

    Apr 16, 2026Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo +1Sequential RecommendationRecommender Systems

    Federal University of São Carlos · Sorocaba, São Paulo, Brazil

  13. GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation

    Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7RL for RecommendationGenerative Retrieval

    JD.com Beijing, China · Waseda University Tokyo, Japan

  14. CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors

    Apr 16, 2026Hang Su, Zequn Liu, Chen Hu +3LLM PersonalizationQuestion Answering

    East China Normal University, China · Zhongguancun Academy, China

  15. CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations

    Apr 16, 2026Xiping Li, Aier Yang, Jianghong Ma +4Graph Neural NetworksRecommender Systems

    Harbin Institute of Technology, China · School of Computer Science, Wuhan University, China

  16. Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization

    Apr 14, 2026Junsoo Park, Youssef Medhat, Htet Phyo Wai +2Recommender SystemsPersonalized Learning

    Georgia Institute of Technology, Atlanta, GA, USA

  17. Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning

    Apr 14, 2026Shanyong Wang, Shuhang Lin, Yining Zhao +2LLM Fine-TuningPersonalized Language Model Alignment

    Rutgers University—New Brunswick · University of Illinois at Urbana-Champaign

  18. Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization

    Apr 13, 2026Zhixin Lin, Jungang Li, Dongliang Xu +5Mobile GUI AgentsPersonalized Language Model Alignment

    Shandong University · The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology +4

  19. MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation

    Apr 12, 2026Hongwei XuMusic RecommendationRecommender Systems

  20. MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations

    Apr 11, 2026Abderaouf Bahi, Mourad Boughaba, Ibtissem Gasmi +2Recommender SystemsUser Preference Modeling

    Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria · Electro-Mechanical Systems Laboratory (LSEM), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria · Mathematical Modeling and Numerical Simulation Laboratory (LAM2SIN), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria

  21. Efficient Personalization of Generative User Interfaces

    Apr 10, 2026Yi-Hao Peng, Jeffrey P. Bigham, Jason WuPairwise Preference LearningHuman Preference Modeling

    Massachusetts Institute of Technology · Carnegie Mellon University · Purdue University

  22. TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

    Apr 10, 2026Qingzhuo Wang, Leilei Wen, Juntao Chen +4Sequential RecommendationRecommender Systems

    Tongji University

  23. KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation

    Apr 9, 2026Tongbo Chen, Zhengxi Lu, Zhan Xu +13Mobile Agent BenchmarksAI Agent Benchmarks

    Zhejiang University · Apple · Tencent

  24. Long-Term Embeddings for Balanced Personalization

    Apr 9, 2026Andrii Dzhoha, Egor MalykhSequential RecommendationUser Preference Modeling

    Zalando SE Berlin, Germany

  25. PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context

    Apr 9, 2026Steven Au, Baihan LinPersonalized Text GenerationGraph Retrieval-Augmented Generation

    Icahn School of Medicine at Mount Sinai

  26. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    Apr 8, 2026Qiyao Ma, Dechen Gao, Rui Cai +4Personalized Reward ModelingReward Model Evaluation

    University of California, Davis

  27. SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests

    Apr 6, 2026Wei Zhou, Yue Shen, Junkai Ji +5Sequential RecommendationUser Preference Modeling

    School of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China · Shenzhen Technology University, Shenzhen, China · College of Computer Science, Chongqing University, Chongqing, China

  28. User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation

    Apr 3, 2026Jing Du, Zesheng Ye, Congbo Ma +2Multimodal RecommendationMultimodal Fusion

    The University of New South Wales, Sydney, Australia · University of Melbourne, Melbourne, Australia · New York University Abu Dhabi, Abu Dhabi, UAE

  29. Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction

    Apr 1, 2026Max Kanwal, Caryn TranPreference AlignmentHuman-AI Interaction

    Stanford University · Northwestern University

  30. Aligning Recommendations with User Popularity Preferences

    Apr 1, 2026Mona Schirmer, Anton Thielmann, Pola Schwöbel +4Popularity BiasPopularity Bias in Recommender Systems

    University of Amsterdam, Amsterdam, Netherlands · Amazon Music, Berlin, Germany · Amazon Music, Seattle, USA