Enhancing conversational recommendations through contrastive user preference modeling

Abstract Conversational recommender systems (CRSs) aim to deliver items that best align with user preferences through interactive, multi-turn dialogues. While recent studies emphasize the value of incorporating sentiment signals into recommendation, most existing methods rely on a single user repres...

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主要な著者:	Bagesh Kumar, Subham Raj, Mayank Anand, Vivek Kumar Verma, Pritesh Tripathi, Anand Jha
フォーマット:	Artigo
言語:	Inglês
出版事項:	Springer 2026-04-01
シリーズ:	Discover Artificial Intelligence
主題:	Conversational recommendation Retrieval-based recommendation Contrastive learning User preference modeling
オンライン･アクセス:	https://doi.org/10.1007/s44163-026-00912-1
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