5 papers
Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
Yuchun Tu, Zhiwei Li, Bingli Sun +2
Federated recommendation facilitates collaborative model training across distributed clients while keeping sensitive user interaction data local. Conventional approaches typically…
Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Jundong Chen, Honglei Zhang, Haoxuan Li +3
Federated recommendation (FR) is a promising paradigm to protect user privacy in recommender systems. Distinct from general federated scenarios, FR inherently needs to preserve cli…
Personalized Recommendation Models in Federated Settings: A Survey
Chunxu Zhang, Guodong Long, Zijian Zhang +4
Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experie…
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions
Jing Jiang, Chunxu Zhang, Honglei Zhang +3
Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the convent…
Federated Vision-Language-Recommendation with Personalized Fusion
Zhiwei Li, Guodong Long, Jing Jiang +2
Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). Bringing VLR to user-oriented o…