Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available.

翻译：联邦推荐是一种新型互联网服务架构，旨在联邦环境下提供隐私保护的推荐服务。现有方案通常将分布式推荐算法与隐私保护机制结合，导致服务器端采用重型模型，阻碍了终端设备智能模型的部署。本文提出一种新颖的个性化联邦推荐（PFedRec）框架，学习大量用户特定的轻量级模型以部署在智能设备上，而非在服务器部署重型模型。此外，我们提出一种新的双重个性化机制，有效学习用户和物品两方面的细粒度个性化。整个学习过程被统一建模为一个联邦优化框架。具体而言，与先前在联邦系统中为所有用户共享完全相同物品嵌入的方法不同，双重个性化机制允许对每个用户的物品嵌入进行微调，生成用户特定的物品表示视图，该机制可直接集成到现有联邦推荐方法中实现即时性能提升。在多个基准数据集上的实验验证了PFedRec及双重个性化机制的有效性。此外，我们提供了物品嵌入个性化技术的可视化与深入分析，为联邦环境下推荐系统的设计提供了新见解。相关代码已开源。