TY - GEN
T1 - What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
AU - Lacic, Emanuel
AU - Fadljevic, Leon
AU - Weissenboeck, Franz
AU - Lindstaedt, Stefanie
AU - Kowald, Dominik
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - News recommendation
KW - Popularity bias
KW - User interface
UR - http://www.scopus.com/inward/record.url?scp=85128777265&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99739-7_20
DO - 10.1007/978-3-030-99739-7_20
M3 - Conference paper
SN - 978-3-030-99738-0
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 172
EP - 179
BT - Advances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Proceedings
A2 - Hagen, Matthias
A2 - Verberne, Suzan
A2 - Macdonald, Craig
A2 - Seifert, Christin
A2 - Balog, Krisztian
A2 - Nørvåg, Kjetil
A2 - Setty, Vinay
PB - Springer
T2 - 44th European Conference on Information Retrieval
Y2 - 10 April 2022 through 14 April 2022
ER -