One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems

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 f...

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Autores principales:	Mareike Wieland, Gerret von Nordheim, Katharina Kleinen-von Königslöw
Formato:	article
Lenguaje:	EN
Publicado:	Cogitatio 2021
Materias:	algorithm-based recommenders diversity news recommender design recommender field experiment reliable surprise Communication. Mass media P87-96
Acceso en línea:	https://doaj.org/article/5ba7bd3c13f24717a6351f5acef71914
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id	oai:doaj.org-article:5ba7bd3c13f24717a6351f5acef71914
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spelling	oai:doaj.org-article:5ba7bd3c13f24717a6351f5acef719142021-11-18T11:14:13ZOne Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems2183-243910.17645/mac.v9i4.4241https://doaj.org/article/5ba7bd3c13f24717a6351f5acef719142021-11-01T00:00:00Zhttps://www.cogitatiopress.com/mediaandcommunication/article/view/4241https://doaj.org/toc/2183-2439Journalistic 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.) may affect these evaluations. The core piece of our survey is a self-programmed recommendation system that accesses a database of vectorized news articles. Respondents search for a personally relevant keyword and select a suitable article, after which another article is recommended automatically, at random, using one of three similarity settings. Our findings show that users prefer recommendations of the most similar articles, which are at the same time perceived as novel, but not necessarily unexpected. However, user evaluations will differ depending on personal characteristics such as formal education, the civic information norm, and the need for cognitive closure.Mareike WielandGerret von NordheimKatharina Kleinen-von KönigslöwCogitatioarticlealgorithm-based recommendersdiversitynews recommender designrecommender field experimentreliable surpriseCommunication. Mass mediaP87-96ENMedia and Communication, Vol 9, Iss 4, Pp 208-221 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	algorithm-based recommenders diversity news recommender design recommender field experiment reliable surprise Communication. Mass media P87-96
spellingShingle	algorithm-based recommenders diversity news recommender design recommender field experiment reliable surprise Communication. Mass media P87-96 Mareike Wieland Gerret von Nordheim Katharina Kleinen-von Königslöw One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
description	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.) may affect these evaluations. The core piece of our survey is a self-programmed recommendation system that accesses a database of vectorized news articles. Respondents search for a personally relevant keyword and select a suitable article, after which another article is recommended automatically, at random, using one of three similarity settings. Our findings show that users prefer recommendations of the most similar articles, which are at the same time perceived as novel, but not necessarily unexpected. However, user evaluations will differ depending on personal characteristics such as formal education, the civic information norm, and the need for cognitive closure.
format	article
author	Mareike Wieland Gerret von Nordheim Katharina Kleinen-von Königslöw
author_facet	Mareike Wieland Gerret von Nordheim Katharina Kleinen-von Königslöw
author_sort	Mareike Wieland
title	One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
title_short	One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
title_full	One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
title_fullStr	One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
title_full_unstemmed	One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
title_sort	one recommender fits all? an exploration of user satisfaction with text-based news recommender systems
publisher	Cogitatio
publishDate	2021
url	https://doaj.org/article/5ba7bd3c13f24717a6351f5acef71914
work_keys_str_mv	AT mareikewieland onerecommenderfitsallanexplorationofusersatisfactionwithtextbasednewsrecommendersystems AT gerretvonnordheim onerecommenderfitsallanexplorationofusersatisfactionwithtextbasednewsrecommendersystems AT katharinakleinenvonkonigslow onerecommenderfitsallanexplorationofusersatisfactionwithtextbasednewsrecommendersystems
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One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems

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