26 citations · 37 across the 3 of their papers we have counts for
4 papers
Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting
Tianyu Zhu, Jiandong Ding, Yansong Shi +2
In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation algorithms to exacerbate popularity bias by disproportionately fav…
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
Yihong Wu, Le Zhang, Fengran Mo +3
Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their d…
History-Aware Conversational Dense Retrieval
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehens…
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
Tianyu Zhu, Yansong Shi, Yuan Zhang +3
Modern recommender systems employ various sequential modules such as self-attention to learn dynamic user interests. However, these methods are less effective in capturing collabor…