  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.
