18 citations · 62 across the 9 of their papers we have counts for
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cs.IR2021★ 3 cited
Multi-Sample based Contrastive Loss for Top-k Recommendation
Hao Tang, Guoshuai Zhao, Yuxia Wu +1
The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key i…
cs.IR2021★ 1 cited
Diversity Regularized Interests Modeling for Recommender Systems
Junmei Hao, Jingcheng Shi, Qing Da +4
With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult…