1.5k citations · 1.9k across the 25 of their papers we have counts for
6 papers · 1 filter
Ensemble Knowledge Distillation for CTR Prediction
Jieming Zhu, Jinyang Liu, Weiqi Li +4
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. Howe…
Self-supervised Graph Learning for Recommendation
Jiancan Wu, Xiang Wang, Fuli Feng +4
Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the succes…
Adversarial Attack on Large Scale Graph
Jintang Li, Tao Xie, Liang Chen +3
Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works…
Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He +4
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user p…
Secure Deep Graph Generation with Link Differential Privacy
Carl Yang, Haonan Wang, Ke Zhang +2
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sen…
A Survey of Adversarial Learning on Graphs
Liang Chen, Jintang Li, Jiaying Peng +6
Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they…