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.

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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.

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Aug 10, 2026Yuting Liu, Wei Wu, Jianzhe Zhao +1LLM Personalization Meta-Learning
Software College, Northeastern University, China · Ant International
UserToolBench: A User-Profile-Hidden Benchmark for Personalized Decision Making in Tool-Use LLMs
Aug 10, 2026Xuexiong Yin, Zechuan Chen, Yongsen Zheng +5Tool-Use Evaluation Tool-Augmented Language Model Agents
Sun Yat-Sen University · Nanyang Technological University · Huawei Noah’s Ark Lab
CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment
Aug 10, 2026Bingcan Guo, Eryue Xu, Jijie Zhou +2Preference Alignment Inference-Time Language Model Alignment
University of Washington · UIUC · Northeastern University
From Product Search to Preference Articulation: The Economics of Agentic Commerce
Aug 9, 2026Lingxiu Dong, Kaiwen Luo, Fasheng XuAgentic Search E-Commerce Search
Olin Business School, Washington University in St. Louis · School of Business, University of Connecticut
Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
Aug 6, 2026Ziyun Xu, Bosen Ding, Yue Zhang +10LLM Reranking Recommender Systems
Meta Platforms Menlo Park, California, USA
GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction
Aug 5, 2026Shuo Liu, Huixiang Cai, Weiru Zhang +1Vision-Language Models VLM Adaptation
Alibaba International Digital Commerce Group
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
Aug 4, 2026Qianqian Wang, Wenwu Gong, Yunshan Li +3Latent Variable Models OOD Generalization
Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Southern University of Science and Technology, Shenzhen, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China · School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
Aug 4, 2026Eugene Lee, Oseong Choi, Byungsoo Kang +1Thompson Sampling Cold-Start Recommendation
NAVER WEBTOON South Korea
PALMs: Using Multi Construct-Grounded Rationales for Modeling Population Preferences in LLMs
Aug 2, 2026Priyanka Dey, Brihi Joshi, Preyashi Poddar +2LLM Alignment Human Preference Modeling
University of Southern California · Information Sciences Institute
Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
Aug 1, 2026Xuefei Wang, Jun Han, Zixuan Wang +4Few-Shot Domain Adaptation LLM Personalization
Beihang University · Nankai University
Know It, Act on It: Investigating Memory Utilization in LLM Personalization
Jul 31, 2026Zhaoxin Feng, Jianfei Ma, Emmanuele ChersoniLLM Personalization LLM Agent Memory
The Hong Kong Polytechnic University
PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction
Jul 31, 2026Huanyu Liu, Baining Chen, Hui Liu +2Semantic ID Recommendation Prototype Learning
Huazhong University of Science and Technology Wuhan, Hubei, China · Central China Normal University Wuhan, Hubei, China
An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks
Jul 30, 2026Sheng Lun Christine Cao, Destenie Nock, Alex DavisDiscrete Choice Modeling User Preference Modeling
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada · Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA, USA +1
Building a User Foundation Model for the Open Web
Jul 30, 2026Solal Vernier, Ivan Can Arisoy, Merwan Barlier +1Self-Supervised Learning CTR Prediction
Teads Ljubljana, Slovenia
Hierarchical Latent Reasoning for LLM-based Recommendation
Jul 30, 2026Peiyu Hu, Siying Gu, Weihai Lu +8Sequential Recommendation RL for Language Model Reasoning
1Xi’an Jiaotong-Liverpool University · 2Xiaohongshu · 3Peking University +1
Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement
Jul 29, 2026Haifeng WuLLM Personalization User Modeling
PayPal
MEMOIR: Temporal Behavioral Memory for Recommendation Across the Preference-Drift Spectrum
Jul 27, 2026Younggue BaeSequential Recommendation Large Language Model-Based Recommendation
Independent Researcher Vancouver, Canada
CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation
Jul 26, 2026Gengyu ZhanMemory-Augmented Language Models Recommender Systems
Shenzhen University
PrefReward: Learning User Preference Matrix for Personalized Text Generation
Jul 23, 2026Yue Wu, Chengbing Wang, Yimeng Bai +3Personalized Language Models Personalized Text Generation
University of Science and Technology of China · The Chinese University of Hong Kong · National University of Singapore
Rushes: A Human Preference Dataset for Pluralistic Alignment
Jul 22, 2026Michael Xu, Jorge Leandro, Sudha Rao +5Sequential Decision Making Human Preference Modeling
Microsoft Research
Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging
Jul 17, 2026Zhichao Yang, Tianjiao Gu, Zhixianhe Zhang +3Personalized Image Aesthetics Assessment Image Quality Assessment
Xidian University Xi’an, China · Xidian University Xian, China
Field-Aware RankMixer with Dual-Stream Bilinear Fusion for the Tencent UNI-REC Challenge
Jul 17, 2026Yufeng Zhang, Zhengqi Xu, Jiajun CuiFeature Interaction Modeling Conversion Rate Prediction
Independent Researcher · Meituan
From Feasibility to Desirability: Plan, Learn, Adapt (PLA) Framework for Personalized On-Device Itinerary Generation
Jul 17, 2026Himel Dev, Tanmoy Sen, Madhusudan Basak +1Travel Planning User Preference Modeling
Tech LLC · University of Virginia · University of Massachusetts Amherst
Deployable Human Preference Alignment in Robotics: Learning Representative Rewards from Diverse Human Preferences
Jul 14, 2026Taehyung Kim, Gwangmo Lee, Minjun Chang +2Clustering Preference Alignment
Yonsei University, Seoul, South Korea · Georgia Institute of Technology, United States of America · Hyundai Motor Company, Seoul, South Korea
SlimPer: Make Personalization Model Slim and Smart
Jul 14, 2026Siqi Wang, Xianjie Chen, Shaofeng Deng +42Learning to Rank Memory-Efficient Inference
RecRec: Recursive Refinement for Sequential Recommendation
Jul 12, 2026Pervez Shaik, Prosenjit Biswas, Abhinav Thorat +2Sequential Recommendation Iterative Refinement
Sony Research India India
APeB: Benchmarking Personalization Ability of Large Language Model Agents
Jul 3, 2026Garry Yang, Zizhe Chen, Xinru Chen +9LLM Personalization LLM Agent Evaluation
1The Chinese University of Hong Kong · 2ByteDance
PAPA: Online Personalized Active Preference Alignment
Jul 1, 2026Anindya Sarkar, Nasik Muhammad Nafi, Isaac Lyngaas +2Diffusion Model Alignment Active Preference Learning
Washington University in St. Louis · Oak Ridge National Laboratory
Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example
Jul 1, 2026Yufei Wu, Daniel SchmiererUser Preference Modeling
Airbnb, Inc. San Francisco, California, USA
Beyond expert users: agents should help users construct preferences, not just elicit them
Jun 29, 2026Irena Saracay, Ludwig Schmidt, Carlos GuestrinAI Agent Benchmarks Recommender Systems
Stanford University

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Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo

Software College, Northeastern University, China · Ant International

LLM Personalization Meta-Learning User Preference Modeling

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Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a particular downstream task. Directly supplying the full preference summary therefore wastes context capacity and introduces cross-task distraction, while manually designing task-specific preference views is difficult to scale. In this work, we study task-specific preference adaptation: given a universal user preference summary and a downstream task, derive a task-conditioned representation that preserves sufficient decision-relevant evidence while removing redundant context. To this end, we propose AlignXada, a training-free meta-learning framework that induces reusable textual refinement policies for adapting universal preference summaries to task-specific ones. The refinement policy is iteratively optimized by a meta learner through verbal reinforcement learning. Across 13 tasks and three downstream models (39 task--model cells), AlignXada achieves an average gain of 3.82 points, improving 33 cells while retaining only 22.8% of the original profile tokens and outperforming RAG in 36 cells. An extended faithfulness analysis further shows that the refined profiles remain largely grounded in the source preferences while preserving task-relevant personalization signals, suggesting that profile-side adaptation serves as a practical complement to universal memory construction for lifelong personalized agents.