2 citations · 2 across the 4 of their papers we have counts for
5 papers
Flattened Graph Convolutional Networks For Recommendation
Yue Xu, Hao Chen, Zengde Deng +2
Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform rec…
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
Hao Chen, Zefan Wang, Yue Xu +2
Cold start is an essential and persistent problem in recommender systems. State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, ba…
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
Hao Chen, Zhong Huang, Yue Xu +4
The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommend…
Learning Single/Multi-Attribute of Object with Symmetry and Group
Yong-Lu Li, Yue Xu, Xinyu Xu +2
Attributes and objects can compose diverse compositions. To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e.g., coupling a…
Non-Recursive Graph Convolutional Networks
Hao Chen, Zengde Deng, Yue Xu +1
Graph Convolutional Networks (GCNs) are powerful models for node representation learning tasks. However, the node representation in existing GCN models is usually generated by perf…