发表机构
College of Computer Science, Sichuan University; Institute of Data Science, National University of Singapore; School of Computing and Information Systems, Singapore Management University（四川大学计算机学院; 新加坡国立大学数据科学研究院; 新加坡管理大学计算与信息系统学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对会话式推荐系统的用户偏好追踪问题，提出双节点蒙特卡洛树搜索的DREAMS框架，经基准数据集实验验证其有效性。
AI 中文摘要
我们研究会话式推荐系统（Conversational Recommendation Systems, CRSs）中会话上下文建模在用户偏好追踪中的作用，为此提出新型树结构化上下文建模框架DREAMS，其明确捕捉多轮交互中用户偏好演化。DREAMS引入两种专门节点类型以支持CRSs的两个基本目标：偏好 elicitation（ elicitation节点利用蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）策略性探索会话动作并推断潜在用户偏好； exploitation节点采用基于大语言模型（LLM）的优化，将追踪的偏好状态转化为用于推荐的结构化检索查询。在基准数据集上的大量实验证明了DREAMS及其设计的有效性。
英文摘要
We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regard, we propose DREAMS, a novel tree-structured context modeling framework that explicitly captures user preference evolution throughout multi-turn interactions. DREAMS introduces two specialized node types to support the two fundamental objectives of CRSs: preference elicitation and preference exploitation. Specifically, elicitation nodes leverage Monte Carlo Tree Search (MCTS) to strategically explore conversational actions and infer latent user preferences, while exploitation nodes employ LLM-based refinement to transform the tracked preference state into structured retrieval queries for recommendation. Extensive experiments on benchmark datasets demonstrate the effectiveness of DREAMS and its design.
CommentsEMNLP 2026 Main Conference
