通过交互式学习实现长期协作中的用户偏好建模

Learning User Preferences Through Interaction for Long-Term Collaboration

January 6, 2026

作者: Shuhaib Mehri, Priyanka Kargupta, Tal August, Dilek Hakkani-Tür

cs.AI

摘要

随着对话智能体在用户协作中积累经验，适应使用者偏好对于培养长期关系及提升协作质量至关重要。我们推出MultiSessionCollab基准测试，用于评估智能体在多轮会话中学习用户偏好并加以运用的能力。为构建适应此场景的智能体，我们开发了配备记忆模块的长期协作智能体，其能随着交互经验的积累持续优化用户偏好模型。研究还表明，通过MultiSessionCollab中的用户模拟器行为可提取训练信号，使智能体生成更全面的反思并优化记忆更新机制。大量实验表明，配备记忆模块的智能体显著提升了长期协作效能，表现为任务成功率提升、交互效率提高以及用户操作负担降低。最后通过真实用户实验验证，记忆功能在现实场景中能有效改善用户体验。

English

As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that persists and refines user preference as interaction experience accumulates. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with memory improves long-term collaboration, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.