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.

翻译：推荐系统中个性化需求的推动在用户隐私与"信息茧房"风险之间形成了张力。尽管联邦学习为隐私保护推荐提供了前景广阔的范式，但其对多样性的影响尚不明确。我们提出了FedFlex——一个两阶段框架，该框架将矩阵分解模型（SVD和BPR）的本地设备端微调与轻量级最大边际相关性（MMR）重排序步骤相结合以提升多样性。我们开展了首个联邦推荐系统的真实用户研究，通过为期两周的在线部署收集行为数据与反馈。结果表明：FedFlex能有效吸引用户，其中BPR在点击率方面优于SVD；MMR重排序持续提升了两种模型的排序质量（nDCG），且具有统计显著性增益，对BPR尤为明显。多样性效应存在差异：MMR提高了两种模型的覆盖率并改善了BPR的列表内多样性，但对SVD的列表内多样性略有降低，这暗示不同模型间个性化与多样化机制存在差异交互。退出问卷反馈显示，大多数用户对重排序列表与原始列表未表现出明确偏好，表明多样性的提升并未显著降低用户满意度。