Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human judgment as a black box. We introduce PrefPalette, a framework that decomposes preferences into attribute dimensions and tailors its preference prediction to distinct social community values in a human-interpretable manner. PrefPalette operationalizes a cognitive science principle known as multi-attribute decision making in two ways: (1) a scalable counterfactual attribute synthesis step that involves generating synthetic training data to isolate for individual attribute effects (e.g., formality, humor, cultural values), and (2) attention-based preference modeling that learns how different social communities dynamically weight these attributes. This approach moves beyond aggregate preference modeling to capture the diverse evaluation frameworks that drive human judgment. When evaluated on 45 social communities from the online platform Reddit, PrefPalette outperforms GPT-4o by 46.6% in average prediction accuracy. Beyond raw predictive improvements, PrefPalette also shed light on intuitive, community-specific profiles: scholarly communities prioritize verbosity and stimulation, conflict-oriented communities value sarcasm and directness, and support-based communities emphasize empathy. By modeling the attribute-mediated structure of human judgment, PrefPalette delivers both superior preference modeling and transparent, interpretable insights, and serves as a first step toward more trustworthy, value-aware personalized applications.
ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing
School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
University of Science and Technology of China（中国科学技术大学）；City University of Hong Kong（香港城市大学）；Northeastern University（东北大学）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）科技有限公司）
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Tongji University（同济大学）；JD.COM Silicon Valley Research Center（京东硅谷研究中心）；Squirrel AI Learning（松鼠AI学习）；Middlesex School（米德尔塞克斯中学）；Simon Fraser University（西蒙弗雷泽大学）
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；Université de Montréal（蒙特利尔大学）
Interpretability Is Not Just a Tax: Sparsity and Diversity Yield Legible and Composable Reward Experts for Personalization
Saarland University（萨尔兰大学）；Independent Researcher（独立研究者）；Bielefeld University（比勒菲尔德大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）
One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
The Hong Kong Polytechnic University（香港理工大学）；Huawei Technologies Ltd.（华为技术有限公司）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）
PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
School of Computer Science（计算机科学学院）；Northwestern Polytechnical University（西北工业大学）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学应用研究所）；Department of Electronic Engineering（电子工程系）；Tsinghua University（清华大学）；School of Cybersecurity（网络安全学院）
PG$^2$Net: Personalized and Group Preferences Guided Network for Next Place Prediction
College of Information, Mechanical, and Electrical Engineering, Shanghai Normal University（上海师范大学信息与机电工程学院）；School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；Shanghai Municipal Country-rural Construction and Transportation Institute（上海市城乡建设和交通研究院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
Graduate School of AI, POSTECH, Pohang, Republic of Korea（POSTECH人工智能研究生院）；Department of CSE, POSTECH, Pohang, Republic of Korea（POSTECH计算机科学与工程系）；National AI Research Lab, Seoul, Republic of Korea（首尔国家人工智能研究实验室）
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Institute of Engineering Research, Korea University（韩国大学工程研究所）；Kim Jaechul Graduate School of AI, KAIST（韩国科学技术院金在哲人工智能研究生院）；Industrial Management Engineering, Korea University（韩国大学产业管理工程学院）
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
GrabTaxi Holdings（GrabTaxi控股公司）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；National University of Singapore（新加坡国立大学）；Grab-NUS AI Lab（Grab-新加坡国立大学人工智能实验室）
PEToolLLM: Towards Personalized Tool Learning in Large Language Models
The Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
