  With the explosion of online news, personalized news recommendation becomes
increasingly important for online news platforms to help their users find
interesting information. Existing news recommendation methods achieve
personalization by building accurate news representations from news content and
user representations from their direct interactions with news (e.g., click),
while ignoring the high-order relatedness between users and news. Here we
propose a news recommendation method which can enhance the representation
learning of users and news by modeling their relatedness in a graph setting. In
our method, users and news are both viewed as nodes in a bipartite graph
constructed from historical user click behaviors. For news representations, a
transformer architecture is first exploited to build news semantic
representations. Then we combine it with the information from neighbor news in
the graph via a graph attention network. For user representations, we not only
represent users from their historically clicked news, but also attentively
incorporate the representations of their neighbor users in the graph. Improved
performances on a large-scale real-world dataset validate the effectiveness of
our proposed method.
