用协同过滤预测用户下一个问题，让对话系统更懂人的兴趣变化
Can We Predict the Next Question? A Collaborative Filtering Approach to Modeling User Behavior
- 结合个人记忆与用户相似性，动态建模提问序列
- 在真实对话数据上提升预测准确率，生成更自然的提问模式
- 适合构建主动响应的智能对话系统
近年来，大语言模型在语言理解和生成方面表现优异，广泛应用于对话和推荐系统。然而，现有系统多静态建模用户偏好，难以捕捉交互行为的动态性和序列特征。用户历史提问序列蕴含丰富的兴趣演化与认知模式信号，但语言建模与行为序列建模之间存在天然断层，制约了其在预测任务中的应用。为此，我们提出协同过滤增强的问题预测框架（CFQP），通过融合个性化记忆模块与基于图的偏好传播机制，动态建模用户-问题交互演化过程。该双重机制使系统能从个体历史中自适应学习，并借助相似用户间的协同信号优化预测。实验表明，所提方法能有效生成模拟真实用户提问模式的智能体，展现出构建主动、自适应对话系统的潜力。
原文摘要 · Abstract (English)
In recent years, large language models (LLMs) have excelled in language understanding and generation, powering advanced dialogue and recommendation systems. However, a significant limitation persists: these systems often model user preferences statically, failing to capture the dynamic and sequential nature of interactive behaviors. The sequence of a user's historical questions provides a rich, implicit signal of evolving interests and cognitive patterns, yet leveraging this temporal data for predictive tasks remains challenging due to the inherent disconnect between language modeling and behavioral sequence modeling. To bridge this gap, we propose a Collaborative Filtering-enhanced Question Prediction (CFQP) framework. CFQP dynamically models evolving user-question interactions by integrating personalized memory modules with graph-based preference propagation. This dual mechanism allows the system to adaptively learn from user-specific histories while refining predictions through collaborative signals from similar users. Experimental results demonstrate that our approach effectively generates agents that mimic real-user questioning patterns, highlighting its potential for building proactive and adaptive dialogue systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。