Understanding the Effects of Personalized Recommender Systems on Political News Perceptions: A Comparison of Content-Based, Collaborative, and Editorial Choice-Based News Recommender System

With the increasing implementation of algorithms across various news platforms, understanding news consumers' subjective perceptions of algorithmic-based news recommender systems has become critical. A between-subjects experiment (News Recommender System type: content-based filtering vs. collab...

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Vydané v:	Journal of broadcasting & electronic media Ročník 67; číslo 3; s. 294 - 322
Hlavný autor:	Liao, Mengqi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia Routledge 27.05.2023 Routledge, Taylor & Francis Group
Predmet:	Algorithms Between-subjects design Collaboration Consumers Filtration Heuristic News Perceptions Recommender systems
ISSN:	0883-8151, 1550-6878
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