Incorporating Knowledge and Content Information to Boost News Recommendation.

News recommendation, which aims to help users find the news they are interested in, is essential for online news platforms to alleviate the information overload problem. News is full of textual information with some knowledge entities, so recent studies try to leverage knowledge graphs (KGs) as side information to better model user preferences over news. However, most knowledge-enhanced methods assume that users are interested in the knowledge entities that occurred in the news. In real scenarios, users may like the news because of the news content rather than the knowledge entities. To take both knowledge and content factors into consideration, we propose a news recommendation method, namely knowledge and content aware network for news recommendation (KCNR). KCNR represents user and news in terms of knowledge and content, then it predicts the weight of user preferences on knowledge and content via a user preferences prediction mechanism. Besides, based on the weight of user preferences on knowledge, it extends user preferences along with entities in knowledge graphs. Experiments on two real-world datasets show that our approach achieves significant improvements over several state-of-the-art baselines in news recommendation.