8 papers
From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation
Jundong Chen, Honglei Zhang, Xiangmou Qu +3
Cross-market recommendation (CMR) aims to enhance recommendation performance across multiple markets. Due to its inherent characteristics, i.e., data isolation, non-overlapping use…
FedUTR: Federated Recommendation with Augmented Universal Textual Representation for Sparse Interaction Scenarios
Kang Fu, Honglei Zhang, Zikai Zhang +5
Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm, attracting considerable attention driven by rising demands for data security. Existing FRs…
Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Jundong Chen, Honglei Zhang, Chunxu Zhang +2
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy.…
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…
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
Zhiwei Li, Guodong Long, Chunxu Zhang +3
Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy c…
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…