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%.

目标级情感分类任务是要得到句子中特定评价目标的情感倾向。一个评论句中往往存在多个目标,多个目标的情感可能一致,也可能不一致。但在已有针对目标级情感分类的评测数据集中:1)大多数是一个句子一个目标;2)在少数有多个目标的句子中,多个目标情感倾向分布很不均衡,多个目标情感一致的情形占较大优势。数据集本身的缺陷限制了模型针对多个目标进行情感分类的提升空间。针对以上问题,本文构建了一个针对多目标情感分类的中文数据集,人工标注了6339个评价目标,共2071条数据。该数据集:1)评价目标个数分布平衡;2)情感正负极性分布平衡;3)多目标情感倾向分布平衡。随后,本文利用多个目标情感分类的主流模型在该数据集上进行了实验与比较分析。结果表明现有主流模型尚不能对存在多个目标且目标情感倾向性不一致实例中的目标进行很好的分类,尤其是目标的情感倾向为中性时。多目标情感分类任务具有一定的难度与挑战性。