Author

Yang Deng

2 papers indexed here

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Open access Jul 2026

Modeling Response Uncertainty and Item Relevance in Conversational Recommender Systems

Conversational Recommender Systems (CRSs) aim to extract user preferences from natural language interactions and deliver personalized recommendations through conversations. In multi-turn conversations, these systems often generate low-confidence responses when user intent is unclear, leading to inappropriate replies an...

Chang-Bin Zhong, Jie Zou, Aixin Sun et al. · ACM Transactions on Informat... · 0 citations

#artificial intelligence Preprint Sep 2026

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

DREAMS is proposed, a novel tree-structured context modeling framework that explicitly captures user preference evolution throughout multi-turn interactions and introduces two specialized node types to support the two fundamental objectives of CRSs: preference elicitation and preference exploitation.

Jin-Cheng Zhang, Chen Huang, Wen-Qiang Lei et al. · 0 citations