Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Wu, Yunfan"'
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowing attackers to insert fake users into the system's training data to s
Externí odkaz:
http://arxiv.org/abs/2409.17476
Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items. Current attack meth
Externí odkaz:
http://arxiv.org/abs/2408.10666
Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of sequential recommender systems renders them vulnerable to poisoning attacks
Externí odkaz:
http://arxiv.org/abs/2401.17723
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious inf
Externí odkaz:
http://arxiv.org/abs/2309.02057
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suffer from severe performance decline under unseen attacks, due to eithe
Externí odkaz:
http://arxiv.org/abs/2305.15792
Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations exposure of each item is equal or propo
Externí odkaz:
http://arxiv.org/abs/2305.05204
Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modification. In this paper, we propose and formulate graph adversarial immunizat
Externí odkaz:
http://arxiv.org/abs/2302.08051
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practica
Externí odkaz:
http://arxiv.org/abs/2208.01819
Quantum sensors are used for precision timekeeping, field sensing, and quantum communication. Comparisons among a distributed network of these sensors are capable of, for example, synchronizing clocks at different locations. The performance of a sens
Externí odkaz:
http://arxiv.org/abs/2205.06382
Publikováno v:
In Information Sciences October 2024 680