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Making sense of recommendations
Author(s) -
Yeomans Michael,
Shah Anuj,
Mullainathan Sendhil,
Kleinberg Jon
Publication year - 2019
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/bdm.2118
Subject(s) - recommender system , computer science , domain (mathematical analysis) , process (computing) , scale (ratio) , artificial intelligence , data science , human–computer interaction , world wide web , mathematics , mathematical analysis , physics , quantum mechanics , operating system
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.