Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News Recommendation

Yingzhi Miao, Martin Pavlovski, Zhiqiang Chen, Fang Zhou

Abstract: Personalized news recommendation is a crucial technique to help users find the content of interest from massive news. While most news recommendation approaches learn a single representation for both users and news, they overlook the nuanced diversity of user interests. Some recent works focused on learning multi-aspect representations of user interests. However, they ignore that news can encompass various aspects of a user’s interests, failing to capture the intricate interactions between news content and user preferences. Meanwhile, a user could occasionally click on some news by mistake. In this paper, we propose a novel news recommendation model which learns disentangled representations for both user interests and news content. This allows for capturing the characteristics of different aspects of news content and user interests. An aspect-wise matching is then applied to capture the fine-grained interactions between news and users. A disentanglement loss is proposed to encourage independence of different aspects. Furthermore, we leverage contrastive learning on a news-level to emphasize the aspect-related information as well as on a user-level to mitigate the impact of misclicked news and thus further improve the model’s robustness. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.