78 citations · 277 across the 32 of their papers we have counts for
6 papers · 1 filter
Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun +4
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical…
What makes a successful rebuttal in computer science conferences? : A perspective on social interaction
Junjie Huang, Win-bin Huang, Yi Bu +3
With an exponential increase in submissions to top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review…
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
Yuanhao Liu, Qi Cao, Huawei Shen +3
Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring…
IDEA: Invariant Defense for Graph Adversarial Robustness
Shuchang Tao, Qi Cao, Huawei Shen +3
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suf…
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
Junjie Huang, Qi Cao, Ruobing Xie +4
Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-ba…
Graph Adversarial Immunization for Certifiable Robustness
Shuchang Tao, Huawei Shen, Qi Cao +3
Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modificatio…