One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
Abstract: Journalistic media increasingly address changing user behaviour online by implementing algorithmic recommendations on their pages. While social media extensively rely on user data for personalized recommendations, journalistic media may choose to aim to improve the user experience based on textual features such as thematic similarity. From a societal viewpoint, these recommendations should be as diverse as possible. Users, however, tend to prefer recommendations that enable "serendipity" - the perception of an item as a welcome surprise that strikes just the right balance between more similarly useful but still novel content. By conducting a representative online survey with n = 588 respondents, we investigate how users evaluate algorithmic news recommendations (recommendation satisfaction, as well as perceived novelty and unexpectedness) based on different similarity settings and how individual dispositions (news interest, civic information norm, need for cognitive closure, etc.)
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
-
Veröffentlichungsversion
begutachtet (peer reviewed)
In: Media and Communication ; 9 (2021) 4 ; 208-221
- Ereignis
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Veröffentlichung
- (wo)
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Mannheim
- (wer)
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SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (wann)
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2021
- Urheber
- DOI
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10.17645/mac.v9i4.4241
- URN
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urn:nbn:de:101:1-2023010509431176436527
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:23 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wieland, Mareike
- Nordheim, Gerret von
- Kleinen-von Königslöw, Katharina
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Entstanden
- 2021
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