通过迭代优化从隐式交互流中学习动态用户画像
Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement
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中文总结 AI 辅助
针对现有个性化LLM依赖显式偏好监督的局限，提出IRIS框架，通过隐式交互流迭代优化用户画像，在Reddit AITA数据上的100位作者测试中，其决策预测准确率达61.0%，优于多种基线方法。
中文摘要 AI 辅助
将大语言模型（LLM）个性化适配到单个用户是提升用户体验的关键，但现有方法通常依赖显式偏好监督，如成对比较或人口统计属性，限制了其在自然交互场景中的适用性。我们提出IRIS框架，该框架无需显式反馈，直接从隐式交互流中学习动态用户画像，方法是从日常对话中提取行为信号，并通过预测驱动的闭环迭代优化画像表示。我们引入了基于行为预测、画像稳定性和决策预测的评估协议。一项基于公共领域自传文本生成的合成交互流的概念验证研究表明，IRIS生成的画像稳定且能区分不同用户，同时揭示了仅记忆方法在面向召回的指标上的局限性。随后，我们在匿名的真实Reddit r/AmItheAsshole（AITA）数据上验证了IRIS，画像仅基于每位作者的历史交互构建。在100位作者中，IRIS在所有评估方法中达到最高的决策预测准确率（61.0%），优于静态画像、仅记忆检索和无个性化基线。这些结果表明，隐式行为建模为个性化LLM提供了一种可扩展的替代方案，替代显式偏好学习，并为需要不断演化用户模型的自适应对话系统和具身智能体提供了实用基础。
英文摘要
Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback. We introduce an evaluation protocol based on behavior prediction, persona stability, and decision prediction. A proof-of-concept study on a synthetic interaction stream derived from public-domain autobiographical text shows that IRIS produces stable personas and distinguishes individual users while revealing limitations of memory-only approaches on recall-oriented metrics. We then validate IRIS on anonymized real-world Reddit r/AmItheAsshole (AITA) data, with personas built solely from each author's historical interactions. Across 100 authors, IRIS achieves the highest decision prediction accuracy among all evaluated methods (61.0%), outperforming static personas, memory-only retrieval, and no-personalization baselines. These results suggest that implicit behavioral modeling provides a scalable alternative to explicit preference learning for personalized LLMs and offers a practical foundation for adaptive conversational systems and embodied agents that require continuously evolving models of their users.
发表机构
- PayPal（贝宝）
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