Personalized Federated Recommendation with Multi-Faceted User Representation and Global Consistent Prototype

Jiaming Qian, Xinting Liao, Xiangmou Qu, Zhihui Fu, Xingyu Lou, Changwang Zhang, Pengyang Zhou, Zijun Zhou, Jun Wang, Chaochao Chen

Abstract: Personalized recommender systems are critical for enhancing user engagement across a range of digital platforms. However, conventional approaches rely heavily on centralized data collection, raising significant privacy concerns. Federated recommender systems (PFRS) address these concerns by decentralizing model training, ensuring user data privacy. Despite the progress, existing methods still struggle with capturing the multi-faceted nature of user and transferring global knowledge effectively. In this work, we propose FedMUR, a novel federated recommendation framework that models user representation as a Gaussian mixture distribution, capturing users' multi-faceted characteristics. Each Gaussian component corresponds to a distinct interest facet, with adaptive mixture weights representing the user's preference intensity toward each facet. To facilitate knowledge transfer, FedMUR constructs global consistent prototypes that encode shared behavioral trends across users via popularity-weighted optimal transport. These prototypes enhance local models by injecting global shared patterns into personalized representation learning. Extensive experiments across several real-world datasets demonstrate that FedMUR significantly outperforms existing state-of-the-art federated recommendation systems.

External IDs:doi:10.1145/3746252.3761213