Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation

TL;DR

This paper introduces FCUCR, a federated continual recommendation framework that enhances long-term user personalization by addressing temporal forgetting and data heterogeneity through innovative strategies, ensuring privacy and improved recommendation accuracy.

Contribution

The paper presents a novel federated continual learning framework with time-aware self-distillation and inter-user prototype transfer, advancing personalized recommendation systems under privacy constraints.

Findings

Outperforms existing methods on four public benchmarks.

Effectively mitigates temporal forgetting in federated recommendation.

Enhances collaborative personalization through prototype transfer.

Abstract

User-centric recommendation has become essential for delivering personalized services, as it enables systems to adapt to users' evolving behaviors while respecting their long-term preferences and privacy constraints. Although federated learning offers a promising alternative to centralized training, existing approaches largely overlook user behavior dynamics, leading to temporal forgetting and weakened collaborative personalization. In this work, we propose FCUCR, a federated continual recommendation framework designed to support long-term personalization in a privacy-preserving manner. To address temporal forgetting, we introduce a time-aware self-distillation strategy that implicitly retains historical preferences during local model updates. To tackle collaborative personalization under heterogeneous user data, we design an inter-user prototype transfer mechanism that enriches each client's representation using knowledge from similar users while preserving individual decision logic. Extensive experiments on four public benchmarks demonstrate the superior effectiveness of our approach, along with strong compatibility and practical applicability. Code is available.