9 citations · 24 across the 11 of their papers we have counts for
5 papers · 1 filter
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.…
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
Bowen Hao, Chaoqun Yang, Lei Guo +2
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target d…
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…
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…
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…