Making sense of recommendations. (14th February 2019)

• Record Type: Journal Article
• Authors: Yeomans, Michael; Shah, Anuj; Mullainathan, Sendhil; Kleinberg, Jon
• Is Part Of: Journal of behavioral decision making. Volume 32:Number 4(2019)
• Page Start: 403
• Page End: 414
• Publication Date: 2019-02-14
• Subjects: algorithms -- decision‐making -- machine learning -- recommendations; Decision making -- Periodicals; Managerial economics -- Periodicals; Behavior -- Periodicals; Decision Making -- Periodicals; Prise de décision -- Périodiques; Économie d'entreprise -- Périodiques; 153.83
• DOI: 10.1002/bdm.2118
• Languages: English
• ISSNs: 0894-3257

Abstract

Abstract: Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer recommender systems to human recommenders in a domain that affords humans many advantages: predicting which jokes people will find funny. We find that recommender systems outperform humans, whether strangers, friends, or family. Yet people are averse to relying on these recommender systems. This aversion partly stems from the fact that people believe the human recommendation process is easier to understand. It is not enough for recommender systems to be accurate, they must also be understood.