79 citations · 119 across the 5 of their papers we have counts for
5 papers
Graph Contrastive Learning with Generative Adversarial Network
Cheng Wu, Chaokun Wang, Jingcao Xu +5
Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with t…
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Ziyang Liu, Chaokun Wang, Jingcao Xu +5
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growi…
Instant Representation Learning for Recommendation over Large Dynamic Graphs
Cheng Wu, Chaokun Wang, Jingcao Xu +7
Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendat…
Multi-behavior Self-supervised Learning for Recommendation
Jingcao Xu, Chaokun Wang, Cheng Wu +6
Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understa…
HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks
Tiankai Gu, Chaokun Wang, Cheng Wu +6
Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse int…