  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.
