Popularity-Enhanced News Recommendation with Multi-View Interest Representation

News recommendation is of vital importance to alleviating in-formation overload. Recent research shows that precise modeling of news content and user interests become critical for news rec-ommendation. Existing methods usually utilize information such as news title, abstract, entities to predict Click Through Rate(CTR) or add some auxiliary tasks to a multi-task learning framework. However, none of them directly consider predicted news popularity and the degree of users' attention to popular news into the CTR prediction results. Meanwhile, multiple inter-ests may arise throughout users' browsing history. Thus it is hard to represent user interests via a single user vector. In this paper, we propose PENR, a Popularity-Enhanced News Recommenda-tion method, which integrates popularity prediction task to im-prove the performance of the news encoder. News popularity score is predicted and added to the final CTR, while news popu-larity is utilized to model the degree of users' tendency to follow hot news. Moreover, user interests are modeled from different perspectives via a subspace projection method that assembles the browsing history to multiple subspaces. In this way, we capture users' multi-view interest representations. Experiments on a real-world dataset validate the effectiveness of our PENR approach.