Making sense of recommendations

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 hu...

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Bibliographic Details
Published inJournal of behavioral decision making Vol. 32; no. 4; pp. 403 - 414
Main Authors Yeomans, Michael, Shah, Anuj, Mullainathan, Sendhil, Kleinberg, Jon
Format Journal Article
LanguageEnglish
Published Chichester Wiley Periodicals Inc 01.10.2019
Subjects
Algorithms
Aversion
decision‐making
Friendship
Jokes
machine learning
recommendations
Recommender systems
Strangers
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Summary: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.
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ISSN:0894-3257
1099-0771
DOI:10.1002/bdm.2118