6 papers
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
Chunxu Zhang, Weipeng Zhang, Guodong Long +3
Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federat…
TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
Mingyuan Xia, Chunxu Zhang, Zijian Zhang +4
Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the comple…
Distilling A Universal Expert from Clustered Federated Learning
Zeqi Leng, Chunxu Zhang, Guodong Long +2
Clustered Federated Learning (CFL) addresses the challenges posed by non-IID data by training multiple group- or cluster-specific expert models. However, existing methods often ove…
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…
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Chunxu Zhang, Guodong Long, Hongkuan Guo +5
Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, p…
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…