  Personalized news recommendation is very important for online news platforms
to help users find interested news and improve user experience. News and user
representation learning is critical for news recommendation. Existing news
recommendation methods usually learn these representations based on single news
information, e.g., title, which may be insufficient. In this paper we propose a
neural news recommendation approach which can learn informative representations
of users and news by exploiting different kinds of news information. The core
of our approach is a news encoder and a user encoder. In the news encoder we
propose an attentive multi-view learning model to learn unified news
representations from titles, bodies and topic categories by regarding them as
different views of news. In addition, we apply both word-level and view-level
attention mechanism to news encoder to select important words and views for
learning informative news representations. In the user encoder we learn the
representations of users based on their browsed news and apply attention
mechanism to select informative news for user representation learning.
Extensive experiments on a real-world dataset show our approach can effectively
improve the performance of news recommendation.
