  News recommendation is important for personalized online news services. Most
existing news recommendation methods rely on centrally stored user behavior
data to both train models offline and provide online recommendation services.
However, user data is usually highly privacy-sensitive, and centrally storing
them may raise privacy concerns and risks. In this paper, we propose a unified
news recommendation framework, which can utilize user data locally stored in
user clients to train models and serve users in a privacy-preserving way.
Following a widely used paradigm in real-world recommender systems, our
framework contains two stages. The first one is for candidate news generation
(i.e., recall) and the second one is for candidate news ranking (i.e.,
ranking). At the recall stage, each client locally learns multiple interest
representations from clicked news to comprehensively model user interests.
These representations are uploaded to the server to recall candidate news from
a large news pool, which are further distributed to the user client at the
ranking stage for personalized news display. In addition, we propose an
interest decomposer-aggregator method with perturbation noise to better protect
private user information encoded in user interest representations. Besides, we
collaboratively train both recall and ranking models on the data decentralized
in a large number of user clients in a privacy-preserving way. Experiments on
two real-world news datasets show that our method can outperform baseline
methods and effectively protect user privacy.
