  The most important task in personalized news recommendation is accurate
matching between candidate news and user interest. Most of existing news
recommendation methods model candidate news from its textual content and user
interest from their clicked news in an independent way. However, a news article
may cover multiple aspects and entities, and a user usually has different kinds
of interest. Independent modeling of candidate news and user interest may lead
to inferior matching between news and users. In this paper, we propose a
knowledge-aware interactive matching method for news recommendation. Our method
interactively models candidate news and user interest to facilitate their
accurate matching. We design a knowledge-aware news co-encoder to interactively
learn representations for both clicked news and candidate news by capturing
their relatedness in both semantic and entities with the help of knowledge
graphs. We also design a user-news co-encoder to learn candidate news-aware
user interest representation and user-aware candidate news representation for
better interest matching. Experiments on two real-world datasets validate that
our method can effectively improve the performance of news recommendation.
