Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity

TLDR

The debate over filter bubbles in algorithmic news recommendation has neglected the core concepts of diversity and algorithms in social science research. The study investigates how multiple recommender systems affect various dimensions of diversity by mapping the values that diversity can serve and defining criteria for a diverse information offer. The authors evaluate recommender systems using simulated article recommendations from a major Dutch broadsheet newspaper, covering 21,973 articles and 500 users. All examined recommendation logics produced recommendation sets as diverse as those curated by human editors, and user‑history‑based recommendations significantly increased topic diversity.

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.