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Personalized news recommendation methods are widely used in online newsservices. These methods usually recommend news based on the matching betweennews content and user interest inferred from historical behaviors. However,these methods usually have difficulties in making accurate recommendations tocold-start users, and tend to recommend similar news with those users haveread. In general, popular news usually contain important information and canattract users with different interests. Besides, they are usually diverse incontent and topic. Thus, in this paper we propose to incorporate newspopularity information to alleviate the cold-start and diversity problems forpersonalized news recommendation. In our method, the ranking score forrecommending a candidate news to a target user is the combination of apersonalized matching score and a news popularity score. The former is used tocapture the personalized user interest in news. The latter is used to measuretime-aware popularity of candidate news, which is predicted based on newscontent, recency, and real-time CTR using a unified framework. Besides, wepropose a popularity-aware user encoder to eliminate the popularity bias inuser behaviors for accurate interest modeling. Experiments on two real-worlddatasets show our method can effectively improve the accuracy and diversity fornews recommendation.

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity