Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Zhang, Kaike"'
Autor:
Zhang, Kaike, Wu, Yunfan, lyu, Yougang, Su, Du, Ge, Yingqiang, Liu, Shuchang, Cao, Qi, Ren, Zhaochun, Sun, Fei
Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairne
Externí odkaz:
http://arxiv.org/abs/2411.14760
Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filteri
Externí odkaz:
http://arxiv.org/abs/2410.22844
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
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and t
Externí odkaz:
http://arxiv.org/abs/2210.10592
Publikováno v:
In Journal of Environmental Chemical Engineering December 2024 12(6)
Publikováno v:
In Ultrasonics December 2020 108
Publikováno v:
In Computers & Industrial Engineering November 2019 137