[2501.08044] UFGraphFR: Graph Federation Recommendation System based on User Text description features

Computer Science > Machine Learning arXiv:2501.08044 (cs) [Submitted on 14 Jan 2025 (v1), last revised 2 Mar 2026 (this version, v5)] Title:UFGraphFR: Graph Federation Recommendation System based on User Text description features Authors:Xudong Wang, Qingbo Hao, Yingyuan Xiao View a PDF of the paper titled UFGraphFR: Graph Federation Recommendation System based on User Text description features, by Xudong Wang and 2 other authors View PDF HTML (experimental) Abstract:Federated learning offers a privacy-preserving framework for recommendation systems by enabling local data processing; however, data localization introduces substantial obstacles. Traditional federated recommendation approaches treat each user as an isolated entity, failing to construct global user relationship graphs that capture collaborative signals, which limits the accuracy of recommendations. To address this limitation, we derive insight from the insight that semantic similarity reflects preference. similarity, which can be used to improve the construction of user relationship graphs. This paper proposes UFGraphFR, a novel framework with three key components: 1) On the client side, private structured data is first transformed into text descriptions. These descriptions are then encoded into semantic vectors using pre-trained models; 2) On the server side, user relationship graphs are securely reconstructed using aggregated model weights without accessing raw data, followed by information propagation throu...