Abstract
Personalized news recommendation is important to help users find their interested news and
improve reading experience. A key problem
in news recommendation is learning accurate
user representations to capture their interests.
Users usually have both long-term preferences
and short-term interests. However, existing
news recommendation methods usually learn
single representations of users, which may be
insufficient. In this paper, we propose a neural news recommendation approach which can
learn both long- and short-term user representations. The core of our approach is a news
encoder and a user encoder. In the news encoder, we learn representations of news from
their titles and topic categories, and use attention network to select important words. In the
user encoder, we propose to learn long-term
user representations from the embeddings of
their IDs. In addition, we propose to learn
short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine
long-term and short-term user representations.
The first one is using the long-term user representation to initialize the hidden state of the
GRU network in short-term user representation. The second one is concatenating both
long- and short-term user representations as
a unified user vector. Extensive experiments
on a real-world dataset show our approach can
effectively improve the performance of neural
news recommendation.