Federated Recommendation is a new service architecture providing recommendations without sharing user data with the server. Existing methods deploy a recommendation model on each client and coordinate their training by synchronizing and aggregating item embeddings. However, while users usually hold diverse preferences toward certain items, these methods indiscriminately aggregate item embeddings from all clients, neutralizing underlying user-specific preferences. Such neglect will leave the aggregated embedding less discriminative and hinder personalized recommendations. This paper proposes a novel Graph-guided Personalization framework (GPFedRec) for the federated recommendation. The GPFedRec enhances cross-client collaboration by leveraging an adaptive graph structure to capture the correlation of user preferences. Besides, it guides training processes on clients by formulating them into a unified federated optimization framework, where models can simultaneously use shared and personalized user preferences. Experiments on five benchmark datasets demonstrate GPFedRec's superior performance in providing personalized recommendations.

翻译：联邦推荐是一种新型服务架构，可在不将用户数据共享给服务器的情况下提供推荐。现有方法在每个客户端部署推荐模型，并通过同步和聚合物品嵌入来协调其训练。然而，用户通常对特定物品持有多样化的偏好，这些方法却无差别地聚合所有客户端的物品嵌入，从而中和了底层的用户特定偏好。这种忽视将导致聚合后的嵌入区分性降低，并阻碍个性化推荐。本文提出了一种新颖的图导个性化框架（GPFedRec）用于联邦推荐。GPFedRec通过利用自适应图结构捕捉用户偏好的相关性，增强了跨客户端协作。此外，它通过将客户端训练过程统一表述为一个联邦优化框架来指导训练，使模型能够同时利用共享和个性化的用户偏好。在五个基准数据集上的实验表明，GPFedRec在提供个性化推荐方面具有优越性能。