TCCM: Time and Content-Aware Causal Model for Unbiased News Recommendation

Yewang Chen, Weiyao Ye, Guipeng Xv, Chen Lin, Xiaomin Zhu

Abstract: Popularity bias significantly impacts news recommendation systems, as popular news articles receive more exposure and are often delivered to irrelevant users, resulting in unsatisfactory performance. Existing methods have not adequately addressed the issue of popularity bias in news recommendations, largely due to the neglect of the time factor and the impact of news content on popularity. In this paper, we propose a novel approach called Time and Content-aware Causal Model, namely TCCM. It models the effects of three factors on user interaction behavior, i.e., the time factor, the news popularity, and the matching between news content and user interest. TCCM also estimates news popularity more accurately by incorporating the news content, i.e., the popularity of entity and words. Causal intervention techniques are applied to obtain debiased recommendations. Extensive experiments on well-known benchmark datasets demonstrate that the proposed approach outperforms a range of state-of-the-art techniques.