Abstract
News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical
for learning accurate news and user representations for news recommendation. However, it
is not considered in many existing news recommendation methods. In this paper, we propose a neural news recommendation approach
with topic-aware news representations. The
core of our approach is a topic-aware news encoder and a user encoder. In the news encoder
we learn representations of news from their titles via CNN networks and apply attention networks to select important words. In addition,
we propose to learn topic-aware news representations by jointly training the news encoder
with an auxiliary topic classification task. In
the user encoder we learn the representations
of users from their browsed news and use attention networks to select informative news for
user representation learning. Extensive experiments on a real-world dataset validate the effectiveness of our approach.