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Erschienen in:
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

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

verfasst von : Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie Lindstaedt, Dominik Kowald

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Leveraging Transformer Self Attention Encoder for Crisis Event Detection in Short Texts
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Metadaten
Titel
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
verfasst von
Emanuel Lacic
Leon Fadljevic
Franz Weissenboeck
Stefanie Lindstaedt
Dominik Kowald
Copyright-Jahr
2022
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_20