提出隐私保护的持续推荐框架，解决用户偏好随时间变化的遗忘问题。
Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation
- 用时间感知自蒸馏保留用户历史偏好
- 通过用户间原型迁移提升个性化推荐效果
- 适合需要长期隐私保护推荐的场景
用户中心推荐对提供个性化服务至关重要，能适应用户行为演变，同时尊重其长期偏好和隐私。尽管联邦学习为去中心化训练提供了可行方案，但现有方法大多忽视用户行为动态，导致时间遗忘和协同个性化能力下降。本文提出FCUCR，一种支持隐私保护长期个性化的联邦持续推荐框架。为缓解时间遗忘，引入时间感知自蒸馏策略，在本地模型更新中隐式保留历史偏好；为应对异构用户数据下的协同个性化问题，设计跨用户原型迁移机制，利用相似用户的知识丰富每个客户端的表征，同时保持个体决策逻辑。在四个公开基准上的大量实验表明，该方法效果更优，具备强兼容性和实际应用价值。代码已开源。
原文摘要 · Abstract (English)
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.
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