Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues
Xiaotian Zhang, Yuan Wang, Ruizhe Chen, Zeya Wang, Runchen Hou, Zuozhu Liu

TL;DR
This paper introduces PersonalAgent, a lifelong user-centric system that continuously infers and adapts to individual preferences in dialogues, improving personalization and coherence over time in interactive systems using LLMs.
Contribution
The paper presents PersonalAgent, a novel lifelong personalization framework that dynamically constructs and refines user profiles through dialogue, addressing cold-start and long-term adaptation challenges.
Findings
PersonalAgent outperforms prompt-based and policy baselines in various contexts.
It maintains preference consistency across sessions.
Human evaluation shows natural and coherent preference capturing.
Abstract
The deployment of Large Language Models (LLMs) in interactive systems necessitates a deep alignment with the nuanced and dynamic preferences of individual users. Current alignment techniques predominantly address universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. To bridge this gap, we propose PersonalAgent, a novel user-centric lifelong agent designed to continuously infer and adapt to user preferences. PersonalAgent constructs and dynamically refines a unified user profile by decomposing dialogues into single-turn interactions, framing preference inference as a sequential decision-making task. Experiments show that PersonalAgent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Recommender Systems and Techniques · AI in Service Interactions
