MIRec: Neural News Recommendation with Multi-Interest and Popularity-Aware Modeling

Yuxin Zhang, Gaode Chen, Lei Wang, Xiaobo Guo, Liyue Ren

Abstract: News recommendation is critical for online news services. How to precisely match news content with users’ interests lies in the core of personalized news recommendation. Existing methods mainly learn a unified embedding vector for each user to represent his/her interests. However, users’ diverse interests can not be expressed adequately by a single embedding representation because of the lack of expressiveness. Additionally, incorporating news popularity into news recommendation can effectively improve accuracy since users with different interests are drawn to current popular news. In this paper, we propose a news recommendation method with multi-interest and popularity-aware modeling, named MIRec. We propose a novel news encoder with attentive learning to obtain unified representations of clicked news from the content and popularity. Furthermore, we exploit a popularity-aware multi-interest extractor to generate multi-interest representations of users and eliminate the bias of news popularity in preference modeling. Besides, we design a popularity predictor for candidate news, which measures its popularity based on the popularity features, recency, and interaction rate. Finally, we adopt a gated mechanism based on user multi-interest representation and the popularity of candidate news to make recommendations. Extensive experiments on large-scale benchmark dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods.