Abstract
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