User Preference Modeling

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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

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

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
Apr 1, 2026Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +3Personalized Language Models Personalized Language Model Alignment
University of Illinois Urbana-Champaign · Korea University
PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent
Mar 31, 2026Hongyi Nie, Xunyuan Liu, Yudong Bai +4GUI Agents Mobile GUI Agents
Northwestern Polytechnical University · Tsinghua University · Peking University +1
MemRerank: Preference Memory for Personalized Product Reranking
Mar 31, 2026Zhiyuan Peng, Xuyang Wu, Huaixiao Tou +2LLM Personalization User Preference Modeling
Santa Clara University · Independent Researcher
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
Mar 31, 2026Iordanis Fostiropoulos, Muhammad Rafay Azhar, Abdalaziz Sawwan +8LLM Evaluation User Modeling
Meta Recommendation Systems (MRS)
EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling
Mar 30, 2026Yujie Zhang, Weikang Yuan, Zhuoren Jiang +1User Preference Modeling Personalized Language Model Alignment
Zhejiang University.
Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization
Mar 29, 2026Canran Xiao, Liwei HouRecommender Systems User Preference Modeling
Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong, China · Hunan University Changsha, Hunan, China
Gaze patterns predict preference and confidence in pairwise AI image evaluation
Mar 25, 2026Nikolas Papadopoulos, Shreenithi Navaneethan, Sheng Bai +2Human Preference Evaluation Pairwise Preference Evaluation
Columbia University, New York, NY
OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework
Mar 25, 2026Ben Chen, Siyuan Wang, Yufei Ma +20Generative Retrieval E-Commerce Search
Kuaishou Technology, Beijing, China
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
Mar 24, 2026Shuochen Liu, Junyi Zhu, Long Shu +11Memory Agent Benchmarks Long-Term Conversational Memory
University of Science and Technology of China · ESAT-PSI, KU Leuven · City University of Hong Kong +2
Learning What Matters Now: Dynamic Preference Inference under Contextual Shifts
Mar 24, 2026Xianwei Cao, Dou Quan, Zhenliang Zhang +1Preference Learning Multi-Objective Reinforcement Learning
School of Artificial Intelligence, Xidian University, Xi’an, 710071, China · State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China
PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking
Mar 23, 2026Wei Zhou, Wuyang Li, Junkai Ji +5Recommender Systems Preference Learning
Shenzhen University, Shenzhen, China · Shenzhen Technology University, Shenzhen, China
AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization
Mar 23, 2026Tianyi Li, Zixuan Wang, Guidong Lei +2Recommender Systems Large Language Model-Based Recommendation
Xiamen University, Xiamen, China
User Preference Modeling for Conversational LLM Agents: Weak Rewards from Retrieval-Augmented Interaction
Mar 21, 2026Yuren Hao, Shuhaib Mehri, ChengXiang Zhai +1User Preference Modeling Preference Learning
University of Illinois at Urbana-Champaign
Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation
Mar 21, 2026Zihao Wang, Yuxiang Wei, Xinpeng Zhou +5T2I Generation User Preference Modeling
Harbin Institute of Technology · Duxiaoman
PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management
Mar 20, 2026Xingyu Feng, Chang Sun, Yuzhu Wang +5Language Model-Based Control User Preference Modeling
China University of Geosciences (Beijing) · Shenzhen University · Hong Kong University of Science and Technology +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 Recommendation Large Language Model-Based Recommendation
BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
Mar 17, 2026Sangyeon Yoon, Hyesoo Hong, Wonje Jeung +5Personalized Language Models LLM Evaluation
Yonsei University · LG AI Research
ReFORM: Review-aggregated Profile Generation via LLM with Multi-Factor Attention for Restaurant Recommendation
Mar 17, 2026Moonsoo Park, Seulbeen Je, Donghyeon ParkRecommender Systems Large Language Model-Based Recommendation
University of Southern California, USA · Sejong University, South Korea
RecBundle: A Next-Generation Geometric Paradigm for Explainable Recommender Systems
Mar 17, 2026Hui Wang, Tianzhu Hu, Mingming Li +6Recommender Systems Geometric Representation Learning
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · JD.com, Beijing, China · School of Cyberspace Security, University of Chinese Academy of Sciences, Beijing, China
MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
Mar 15, 2026Yuantong Li, Lei Yuan, Zhihao Zheng +27Debiased ML Recommender Systems
Meta AI
Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
Mar 14, 2026Xiaofei Zhu, Jinfei Chen, Feiyang Yuan +1Recommender Systems Generative Recommendation
Chongqing University of Technology, College of Computer Science and Engineering, Chongqing, China · Chongqing Normal University, College of Computer and Information Science, Chongqing, China
Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback
Mar 13, 2026Gihoon Kim, Euntai KimPreference Learning RL from Human Feedback
Yonsei University · Korea Institute of Science and Technology
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
Mar 12, 2026Dat Tran, Yongce Li, Hannah Clay +3Recommender Systems Preference Elicitation
Stanford University · Massachusetts Institute of Technology · Amazon
LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce
Mar 11, 2026Hao N. Nguyen, Hieu M. Nguyen, Son Van Nguyen +1Recommender Systems Large Language Model-Based Recommendation
Faculty of Computer Science, PHENIKAA University, Hanoi, Vietnam · Faculty of Interdisciplinary Digital Technology, PHENIKAA University, Hanoi, Vietnam · Corresponding author at PHENIKAA University, Hanoi, Vietnam
Modeling Stage-wise Evolution of User Interests for News Recommendation
Mar 11, 2026Zhiyong Cheng, Yike Jin, Zhijie Zhang +3Recommender Systems User Preference Modeling
The School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, China · Institute of Artificial intelligence, Hefei Comprehensive National Science Center, Anhui, China
Social Knowledge for Cross-Domain User Preference Modeling
Mar 10, 2026Nir Lotan, Adir Solomon, Ido Guy +1Cross-Domain Recommendation User Preference Modeling
University of Haifa · Ben-Gurion University of the Negev
LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
Mar 5, 2026Jinwen Chen, Shiwen Zhang, Shuai Gong +6Local Search User Preference Modeling
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing · Meituan
Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
Mar 4, 2026Qianyun Guo, Yibo Li, Yue Liu +1LLM Evaluation LLM Personalization
National University of Singapore, Singapore.
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Mar 4, 2026Yun Lu, Xiaoyu Shi, Hong Xie +2Fairness in Recommender Systems Recommender Systems
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences Chongqing, China · The First Affiliated Hospital, University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong, Hong Kong
Multi-view Attention Fusion of Heterogeneous Hypergraph with Dynamic Behavioral Profiling for Personalized Learning Resource Recommendation
Mar 4, 2026Tao Xie, Yan Li, Yongpan Sheng +1Hypergraph Learning Educational Data Mining
Tao Xie, Yan Li, and Jian Liao are with the Faculty of Education, Southwest University, Chongqing, China 400715 · Yongpan Sheng is with the College of Computer and Information Science, Southwest University, Chongqing, China 400715

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Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

Gyuseok Lee, Wonbin Kweon, Zhenrui Yue, SeongKu Kang+2

University of Illinois Urbana-Champaign · Korea University

Personalized Language Models Personalized Language Model Alignment User Preference Modeling Reward Modeling

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Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, existing methods estimate user weights from scarce data in isolation and as deterministic points, leading to inaccurate and unreliable inference. We introduce Variational Reward Factorization (VRF), an uncertainty-aware framework that represents each user's preferences as a variational distribution in a shared preference space. VRF infers user distributions via a variational encoder, derives weights through Wasserstein distance matching with shared probabilistic bases, and downweights uncertain estimates through a variance-attenuated loss. On three benchmarks, VRF outperforms all baselines across seen and unseen users, few-shot scenarios, and varying uncertainty levels, with gains extending to downstream alignment. Our code is available at https://github.com/Gyu-Seok-Lee/VRF_COLM26.