The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available to ease reproducibility

翻译：联邦推荐系统是一种新兴的人工智能服务架构，以隐私保护的方式提供推荐服务。利用用户关系图增强联邦推荐是一个具有前景的研究方向。然而，如何在联邦设置中构建用户关系图，同时保持基于数据本地性的隐私保护，仍是一个开放挑战。受“相似用户对相同物品集合具有相似视角（嵌入）”这一简单动机启发，本文提出了一种新颖的图引导联邦推荐个性化方法（GPFedRec）。该方法在服务器端基于用户特定的个性化物品嵌入构建用户关系图，而无需访问用户的交互记录。个性化物品嵌入在每个设备端进行本地微调，随后通过度量客户端特定物品嵌入之间的相似性构建用户关系图。由于无需访问用户历史交互数据，本方法实现了经典联邦学习基于数据本地性的隐私保护。此外，本文设计了一种图引导聚合机制，能够同时利用用户关系图与联邦优化框架。在五个基准数据集上的大量实验表明GPFedRec具有优越的性能。深入研究表明，GPFedRec可作为插件普遍提升现有联邦推荐方法的性能，同时确保用户隐私安全。代码已开源以促进可复现性。