6 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.IR2024★ 6 cited
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
Linxin Guo, Yaochen Zhu, Min Gao +3
Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiv…
cs.IR2022★ 1 cited
Addressing the Extreme Cold-Start Problem in Group Recommendation
Guo linxin, Tao yinghui, Gao Min +3
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems…
cs.IR2022★ 3 cited
Efficient Bi-Level Optimization for Recommendation Denoising
Zongwei Wang, Min Gao, Wentao Li +3
The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, impli…