CPJN: News recommendation with a content and popularity joint network

Users may click on a news because they are interested in its content or because the news contains important information and is very popular. Modeling these two aspects is crucial for accurate news recommendation. Most existing studies focused on capturing users' preferences towards news content, and thus they are limited in investigating in depth users' preferences towards news popularity and independently capturing user content and popularity preferences. In this article, we further improve recommendation performance by proposing a news recommendation with content and popularity joint network (CPJN) model. The CPJN contains a content-based network, a popularity-based network, and an adaptive combination network. The content-based network generates a users' preference feature towards news content by eliminating popularity bias in important information extracted from user side information (such as city and age) and uses the information with the eliminated popularity bias to enhance users' preference representation towards news content. The popularity-based network generates a user preference feature towards news popularity by eliminating content bias that is enhanced through news side information (such as category and author). Furthermore, since users exhibit differing degrees of sensitivity towards news popularity, we propose an adaptive combination network to integrate these two preferences for recommendation. Extensive experiments on two real-world datasets demonstrate the effectiveness of CPJN. Compared to the state-of-the-art baseline, CPJN achieved average improvements of 1.493 % in accuracy rate and 1.502 % in recall rate.