10 papers
Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
Zhida Qin, Zemu Liu, Haoyan Fu +4
Cross-domain sequential recommendation (CDSR) alleviates interaction sparsity by jointly modeling user behaviors across multiple domains. While current studies have made some progr…
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…
TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models
Honglei Zhang, Zhiwei Li, Haoxuan Li +3
Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent on-devic…
MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation
Kang Fu, Honglei Zhang, Xuechao Zou +1
Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in rece…
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.…