Journal of Computational Social Scienceに論文が掲載されました．
The diversity of recommender systems has been well analyzed, but the impact of their diversity on user engagement is less understood. Our study is the first to attempt to analyze the relationship between diversity and user engagement in the news domain. In this study, we introduce the notion of popularity diversity, propose metrics for it, and analyze user behavior on popular news applications in terms of content diversity and popularity diversity, the impact of which we find to be closely related to user activity. We also find that users who use these services for longer periods have not only higher content diversity and popularity diversity but also a tendency to increase diversity as the week progresses. Users with low content diversity had a 224% greater withdrawal rate than those with high content diversity, and users with low popularity diversity had a 112% greater withdrawal rate than those with high popularity diversity. Although many studies have examined changes in diversity in recommender systems, we notice this trend is affected by user withdrawal. Finally, we confirm that popularity diversity and content diversity explain user withdrawal and predict user behavior. Our study reveals the relationship between diversity and engagement in the news domain and introduces the impact of popularity bias per user as a metric of diversity.