通过动态检索用户历史，生成个性化画像提升说服力预测效果。
Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction
- 设计可训练的查询生成器与摘要器，自动提取相关历史记录
- 在Reddit数据集上将说服力预测F1从33%提升至47%
- 适合需要个性化说服力分析的推荐与安全评估场景
估计信息的说服力在推荐系统、大模型安全评估等应用中至关重要。尽管需考虑被说服者的价值观、经历和推理风格，但目前尚无系统性框架来优化利用用户过往行为（如对话）以提升说服力预测模型性能。为此，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：一个查询生成器，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；一个画像生成器，将这些记录总结为有效信息输入预测模型。在ChangeMyView Reddit数据集上的实验表明，该方法在多个预测模型上均实现稳定提升，使Llama-3.3-70B-Instruct的F1值从33%提升至47%。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器特异性，而非依赖静态属性或表面相似性。结果强调了面向任务、上下文相关的用户画像对个性化说服力预测的重要性。
原文摘要 · Abstract (English)
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, raising F1 from 33% to 47% on Llama-3.3-70B-Instruct. 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.
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