@inproceedings{chen-etal-2025-deeper,
    title = "{DEEPER} Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling",
    author = "Chen, Aili  and
      Du, Chengyu  and
      Chen, Jiangjie  and
      Xu, Jinghan  and
      Zhang, Yikai  and
      Yuan, Siyu  and
      Chen, Zulong  and
      Li, Liangyue  and
      Xiao, Yanghua",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1177/",
    pages = "24157--24180",
    ISBN = "979-8-89176-251-0",
    abstract = "To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human-readable persona modeling. In dynamic real-world scenarios, effective persona modeling necessitates leveraging streaming behavior data to continually optimize user personas.However, existing methods{---}whether regenerating personas or incrementally extending them with new behaviors{---}often fail to achieve sustained improvements in persona quality or future behavior prediction accuracy. To address this, we propose DEEPER, a novel approach for dynamic persona modeling that enables continual persona optimization. Specifically, we enhance the model{'}s direction-search capability through an iterative reinforcement learning framework, allowing it to automatically identify effective update directions and optimize personas using discrepancies between user behaviors and model predictions.Extensive experiments on dynamic persona modeling involving 4,800 users across 10 domains highlight `s superior persona optimization capabilities, delivering an impressive 32.2{\%} average reduction in user behavior prediction error over four update rounds{---}outperforming the best baseline by a remarkable 22.92{\%}."
}