10 citations · 63 across the 40 of their papers we have counts for
8 papers · 1 filter
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Lijian Chen, Wei Yuan, Tong Chen +3
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. Whi…
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities.…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Wei Yuan, Shilong Yuan, Chaoqun Yang +2
Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns re…
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Lingzhi Wang, Tong Chen, Wei Yuan +3
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about…
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Xuhui Ren, Wei Yuan, Tong Chen +3
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-ba…
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Wei Yuan, Quoc Viet Hung Nguyen, Tieke He +2
Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data. Since all participan…
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen +3
The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In Fed…