What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations

Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie Lindstaedt, Dominik Kowald*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Proceedings
EditorsMatthias Hagen, Suzan Verberne, Craig Macdonald, Christin Seifert, Krisztian Balog, Kjetil Nørvåg, Vinay Setty
PublisherSpringer
Pages172-179
Number of pages8
ISBN (Electronic)978-3-030-99739-7
ISBN (Print)978-3-030-99738-0
DOIs
Publication statusPublished - 2022
Event44th European Conference on Information Retrieval: ECIR 2022 - Stavanger, Norway
Duration: 10 Apr 202214 Apr 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13186 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference44th European Conference on Information Retrieval
Abbreviated titleECIR 2022
Country/TerritoryNorway
CityStavanger
Period10/04/2214/04/22

Keywords

  • News recommendation
  • Popularity bias
  • User interface

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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