8 citations · 26 across the 6 of their papers we have counts for
6 papers
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Mingshi Yan, Fan Liu, Jing Sun +3
In recommender systems, multi-behavior methods have demonstrated their effectiveness in mitigating issues like data sparsity, a common challenge in traditional single-behavior reco…
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Jing Sun +2
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfacto…
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Chen Gao +4
Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one t…
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Baopu Li, Shuhui Yang +6
Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerni…
Sparsely-Labeled Source Assisted Domain Adaptation
Wei Wang, Zhihui Wang, Yuankai Xiang +4
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available…
Importance Filtered Cross-Domain Adaptation
Wei Wang, Haojie Li, Zhihui Wang +3
In Domain Adaptation (DA), the category-relevant losses usually occupy a dominant position, while they are usually built with hard or soft labels in existing models. We observed th…