Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Issue 7 (3rd July 2018)

• Record Type: Journal Article
• Authors: Möller, Judith; Trilling, Damian; Helberger, Natali; van Es, Bram
• Is Part Of: Information, communication & society. Volume 21:Issue 7(2018)
• Page Start: 959
• Page End: 977
• Publication Date: 2018-07-03
• Subjects: News -- recommender systems -- diversity metrics -- filter bubbles -- automated content classification; Information technology -- Social aspects -- Periodicals; Information technology -- Economic aspects -- Periodicals; Information technology -- Political aspects -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; 303.4833
• DOI: 10.1080/1369118X.2018.1444076
• Languages: English
• ISSNs: 1369-118X

Abstract

ABSTRACT: In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users ( N =21, 973 articles, N =500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.