9 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
The 1st Workshop on Human-Centered Recommender Systems
Kaike Zhang, Yunfan Wu, Yougang lyu +6
Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous chall…
Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
Kaike Zhang, Qi Cao, Yunfan Wu +3
Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an ef…
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Yige Yuan, Bingbing Xu, Teng Xiao +4
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at ba…
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Kaike Zhang, Qi Cao, Yunfan Wu +3
Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowi…
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
Yunfan Wu, Qi Cao, Shuchang Tao +3
Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the trai…