232 citations · 532 across the 8 of their papers we have counts for
7 papers
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
Graph Transformer for Recommendation
Chaoliu Li, Lianghao Xia, Xubin Ren +3
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We hi…
Graph-less Collaborative Filtering
Lianghao Xia, Chao Huang, Jiao Shi +1
Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with t…
Disentangled Graph Social Recommendation
Lianghao Xia, Yizhen Shao, Chao Huang +3
Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation r…
Heterogeneous Graph Contrastive Learning for Recommendation
Mengru Chen, Chao Huang, Lianghao Xia +3
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve hetero…
Multi-Behavior Graph Neural Networks for Recommender System
Lianghao Xia, Chao Huang, Yong Xu +2
Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recen…