422 citations · 815 across the 32 of their papers we have counts for
7 papers · 1 filter
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
Yuni Lai, Yulin Zhu, Wenqi Fan +2
The robustness of recommender systems under node injection attacks has garnered significant attention. Recently, GraphRfi, a GNN-based recommender system, was proposed and shown to…
A Comprehensive Survey on Trustworthy Recommender Systems
Wenqi Fan, Xiangyu Zhao, Xiao Chen +8
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalize…
Fairness Reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang +4
Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…
Disentangled Contrastive Learning for Social Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +3
Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the use…
Knowledge-enhanced Black-box Attacks for Recommendations
Jingfan Chen, Wenqi Fan, Guanghui Zhu +4
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles…
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Yuxuan Wan, Han Xu, Xiaorui Liu +3
Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is s…
A Comprehensive Survey on Automated Machine Learning for Recommendations
Bo Chen, Xiangyu Zhao, Yejing Wang +3
Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored…