  Recommender Systems (RSs) have become increasingly important in many
application domains, such as digital marketing. Conventional RSs often need to
collect users' data, centralize them on the server-side, and form a global
model to generate reliable recommendations. However, they suffer from two
critical limitations: the personalization problem that the RSs trained
traditionally may not be customized for individual users, and the privacy
problem that directly sharing user data is not encouraged. We propose
Personalized Federated Recommender Systems (PersonalFR), which introduces a
personalized autoencoder-based recommendation model with Federated Learning
(FL) to address these challenges. PersonalFR guarantees that each user can
learn a personal model from the local dataset and other participating users'
data without sharing local data, data embeddings, or models. PersonalFR
consists of three main components, including AutoEncoder-based RSs (ARSs) that
learn the user-item interactions, Partially Federated Learning (PFL) that
updates the encoder locally and aggregates the decoder on the server-side, and
Partial Compression (PC) that only computes and transmits active model
parameters. Extensive experiments on two real-world datasets demonstrate that
PersonalFR can achieve private and personalized performance comparable to that
trained by centralizing all users' data. Moreover, PersonalFR requires
significantly less computation and communication overhead than standard FL
baselines.
