Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search

arXiv:2609.00618v1 Announce Type: cross 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.

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems
Retrieval, Scoring, and Decoding Shape Performance and Stability in LLM-based Conversational Recommendation
L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation
S-MARC: Causal Streaming Reasoning for Full-Duplex Conversational Behavior Modeling

Source: arXiv cs.AI | 2026-09-02

Related