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
The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker so that the reranker discriminates answer-supporting passages from topical distractors at inference time, without requiring answers.
Shuai Qin, Guo-Jia An, Wei-Kang Guo et al.· 0 citations
Divergent Reasoning for LLM-based Recommendation is proposed, which effectively mitigates the issue of reasoning path collapse, while improving both the accuracy and diversity of LLM-based recommendations.
Guo-Jia An, Jie Zou, Yu-Han Yang et al.· Annual International ACM SIG...· 1 citation
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