Papers with HyperCRS
HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)
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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam
| Challenge: | Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions. |
| Approach: | They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system. |