Beyond Centralization: User-Controlled Federated Recommendations in Practice

• URL: http://arxiv.org/abs/2605.12527v1
• Date: Fri, 10 Apr 2026 19:12:48 GMT
• Title: Beyond Centralization: User-Controlled Federated Recommendations in Practice
• Abstract summary: We present a live federated recommender system that allows users to control the recommendation objective while keeping their data local.<n>In a 53-day deployment with 22 participants and a catalog of 8807 titles, users interacted with recommendations and switched between personalization and diversity-enhanced ranking.<n>Our results show that user control, privacy, and effective personalization can be combined in a working system.
• Score: 46.413429599755936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommendation systems typically require centralized user data, limiting user control and raising privacy concerns. Federated learning offers an alternative by keeping data on-device, but its impact on real user behavior remains largely unexplored. We present a live federated recommender system that allows users to control the recommendation objective while keeping their data local. In a 53-day deployment with 22 participants and a catalog of 8807 titles, users interacted with recommendations and switched between personalization and diversity-enhanced ranking. We find that users prefer personalization when given explicit choice (65.37\% vs.\ 62.07\% CTR), actively engage with control mechanisms (3.93/5 satisfaction; 248 settings changes), and develop an understanding of how their interactions affect recommendations through immediate feedback. Our results show that user control, privacy, and effective personalization can be combined in a working system. We demonstrate a practical approach to interactive, privacy-preserving recommendation. Code and demo materials are available at: https://github.com/SlokomManel/federated-recommendations-participants

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