UFGraphFR：基于用户文本描述特征的图联邦推荐系统
UFGraphFR: Graph Federation Recommendation System based on User Text description features
AI总结：
UFGraphFR通过语义向量、安全用户关系图和个性化行为序列，实现高效的联邦推荐，提升推荐准确性和个性化效果。
摘要：
联邦学习为推荐系统提供了一个隐私保护的框架，通过允许本地数据处理；然而，数据本地化引入了显著的障碍。传统的联邦推荐方法将每个用户视为孤立的实体，无法构建全球用户关系图，无法捕捉协同信号，这限制了推荐的准确性。为了解决这一限制，我们从语义相似性反映偏好相似性的洞察中获得启发，可以用于改进用户关系图的构建。本文提出UFGraphFR，一个新颖的框架，包含三个关键组件：1）在客户端侧，私有结构化数据首先转换为文本描述。这些描述随后使用预训练模型编码成语义向量；2）在服务器侧，使用聚合模型权重安全地重建用户关系图，而不访问原始数据，然后通过轻量级图神经网络进行信息传播；3）在客户端侧，使用Transformer架构个性化用户行为序列。在四个基准数据集上进行的大量实验表明，UFGraphFR在推荐准确性和个性化方面显著优于最先进的基线。该框架在不同预训练模型下也保持了鲁棒性，这通过获得的一致性能指标得到证明。这项工作提供了一种实用的方法，通过使用语义向量、安全的用户关系图和个性化行为序列，实现高效的联邦推荐，同时严格保护隐私。代码可在：https://github.com/trueWangSyutung/UFGraphFR获得。
英文摘要：
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 through lightweight graph neural networks; 3) On the client side, user behavior sequences are personalized using Transformer architectures. Extensive experiments conducted on four benchmark datasets demonstrate that UFGraphFR significantly outperforms state-of-the-art baselines in both recommendation accuracy and personalization. The framework also maintains robustness across different pre-trained models, as evidenced by the consistent performance metrics obtained. This work provides a practical method for efficient federated recommendations with strict privacy by using semantic vectors, secure user relationship graphs, and personalized behavior sequences. The code is available at: https://github.com/trueWangSyutung/UFGraphFR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
