用联邦学习提升推荐多样性，兼顾隐私与个性化。
FedFlex: Federated Learning for Diverse Netflix Recommendations
- 本地微调矩阵分解模型+轻量MMR重排，提升推荐多样性。
- BPR模型点击率优于SVD，MMR显著提升nDCG，尤其对BPR效果明显。
- 用户对重排列表无明显偏好，说明多样性未影响满意度。
个性化推荐系统在提升用户体验的同时，带来隐私泄露与信息茧房的矛盾。联邦学习虽能保护隐私，但其对推荐多样性的影响尚不明确。本文提出FedFlex，一种两阶段框架：先在设备端对SVD和BPR模型进行本地微调，再通过轻量级最大边际相关性（MMR）重排提升多样性。我们在Netflix真实场景中开展首次活体用户研究，持续两周收集行为数据与反馈。结果显示，FedFlex有效吸引用户，BPR模型点击率优于SVD；MMR重排在两个模型上均显著提升nDCG，且对BPR增益更明显。多样性方面，MMR提升了两类模型的覆盖率，改善了BPR的列表内多样性，但略微降低了SVD的内部多样性，表明不同模型在个性化与多样性间的交互机制存在差异。退出问卷显示，多数用户对重排前后列表无明显偏好，说明多样性增强未显著降低用户满意度。
原文摘要 · Abstract (English)
The drive for personalization in recommender systems creates a tension between user privacy and the risk of "filter bubbles". Although federated learning offers a promising paradigm for privacy-preserving recommendations, its impact on diversity remains unclear. We introduce FedFlex, a two-stage framework that combines local, on-device fine-tuning of matrix factorization models (SVD and BPR) with a lightweight Maximal Marginal Relevance (MMR) re-ranking step to promote diversity. We conducted the first live user study of a federated recommender, collecting behavioral data and feedback during a two-week online deployment. Our results show that FedFlex successfully engages users, with BPR outperforming SVD in click-through rate. Re-ranking with MMR consistently improved ranking quality (nDCG) across both models, with statistically significant gains, particularly for BPR. Diversity effects varied: MMR increased coverage for both models and improved intra-list diversity for BPR, but slightly reduced it for SVD, suggesting different interactions between personalization and diversification across models. Our exit questionnaire responses indicated that most users expressed no clear preference between re-ranked and unprocessed lists, implying that increased diversity did not substantially reduce user satisfaction.
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