Unbiased news recommendation model combining time and content

Abstract

The influence of popularity bias on news recommendation systems is substantial, as it increased the exposure of popular news and their being pushes to irrelevant users frequently, leading to less-than-ideal performance of the system. Existing popularity debiasing methods aim to optimize the fairness of recommendations so that unpopular news have more chances to be recommended. However, they ignore the timeliness that characterizes news, and some outdated news are recommended to users due to their low popularity. Unfortunately, users are not interested in outdated news. In addition, existing debiasing methods use content-independent popularity computation, which cannot accurately capture the dynamic changes in news popularity, thus reducing the effectiveness of debiasing. In this research, we introduce an innovative method named Time and Content-aware Causal Model, referred to as TCCM, which mitigates the popularity bias using current causal inference techniques that work well. The difference is that, first, we design a novel causal graph to analyze user interaction considering that news is timeliness. Based on this causal graph, TCCM models the influences of three factors on user interaction, namely, the timeliness of news, the popularity of news, and the matching between the content of news and the interest of the user, in order to solve the problem of recommending outdated news to users after de-biasing. Second, we consider the influences of news content on popularity and propose a new popularity estimation method. In TCCM, we improve the debiasing effect by combining news content (entity and word popularity) to obtain more accurate news popularity. Extensive experiments conducted on widely recognized benchmark datasets show that TCCM surpasses several cutting-edge approaches.