What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
This research addresses the problem of optimizing news recommendations for different user interfaces and mitigating bias for anonymous users on online platforms, though it is incremental in nature.
The study investigated how user interface types and popularity bias mitigation affect news article recommendations on an Austrian online platform, finding that desktop devices had the highest visibility for recommendations while mobile devices had the highest interaction rates, and personalized recommendations reduced popularity bias for anonymous users.
Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users.