Problem
Research questions and friction points this paper is trying to address.
Conversational Recommender Systems
context modeling
user preference tracking
multi-turn interactions
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
🤖 AI Summary
研究通过提出DREAMS框架，利用双节点蒙特卡洛树搜索方法来改进对话推荐系统中的用户偏好追踪问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual-node
Monte Carlo Tree Search
Conversational Recommender Systems
Preference Elicitation
LLM-based Refinement
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