Personalized Federated Contrastive Learning for Recommendation

Abstract

Recommender systems play crucial roles in addressing the issue of information overload, but traditional centralized storage in recommendation poses significant privacy concerns. In recent years, federated learning has been successfully introduced into a recommendation, while these algorithms still encounter several challenges. First, real-world recommendation scenarios often suffer from sparse data, making it difficult for models to learn reliable representations. Second, data heterogeneity necessitates the design of personalized models to enhance recommendation performance. To address these challenges, we propose a federated recommendation approach based on graph neural networks, named federated personalized contrastive learning for recommendation. On the client side, we propose a contrastive learning approach to enhance the embedding quality of nodes (users or items) by maximizing positive similarities. Specifically, we formulate the concept of structural neighbors based on the graph structure and devise a contrastive learning objective. We treat nodes and their structural neighbors as positive pairs to better learn node representations. On the server side, we group users based on the learned representations and compute cluster-level federated models and a global model. Each user learns a personalized model by combining these two models. Extensive experiments on five real-world datasets demonstrate that the proposed algorithm outperforms existing methods in terms of performance.

Topics & Concepts