Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is imperative to consider the target persuadee's characteristics, such as their values, experiences, and reasoning styles, there is currently no established systematic framework to optimize leveraging a persuadee's past activities (e.g., conversations) to the benefit of a persuasiveness prediction model. To address this problem, we propose a context-aware user profiling framework with two trainable components: a query generator that generates optimal queries to retrieve persuasion-relevant records from a user's history, and a profiler that summarizes these records into a profile to effectively inform the persuasiveness prediction model. Our evaluation on the ChangeMyView Reddit dataset shows consistent improvements over existing methods across multiple predictor models, with gains of up to +13.77%p in F1 score. Further analysis shows that effective user profiles are context-dependent and predictor-specific, rather than relying on static attributes or surface-level similarity. Together, these results highlight the importance of task-oriented, context-dependent user profiling for personalized persuasiveness prediction.

翻译：评估消息的说服力在从推荐系统到大型语言模型安全性评估等多种应用中至关重要。尽管必须考虑目标说服对象的特征，如价值观、经历和推理风格，但目前尚无成熟的系统化框架来优化利用说服对象的历史活动（例如对话）以提升说服力预测模型的性能。为解决此问题，我们提出一种上下文感知的用户画像框架，包含两个可训练组件：查询生成器（用于生成最优查询以从用户历史中检索与说服相关的记录）和画像生成器（将这些记录归纳为画像以有效指导说服力预测模型）。我们在ChangeMyView Reddit数据集上的评估表明，该框架在多种预测模型上均优于现有方法，F1分数最高提升达+13.77个百分点。进一步分析显示，有效的用户画像具有上下文依赖性和预测模型特异性，而非依赖于静态属性或表层相似性。这些结果共同凸显了面向任务、上下文依赖的用户画像对于个性化说服力预测的重要性。