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Publications
  • October 2019
  • Article
  • Journal of Behavioral Decision Making

Making Sense of Recommendations

By: Michael Yeomans, Anuj Shah, Sendhil Mullainathan and Jon Kleinberg
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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.

Keywords

Recommender Systems; Artificial Intelligence; Interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes

Citation

Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414.
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More from the Authors
  • A 680,000-Person Megastudy of Nudges to Encourage Vaccination in Pharmacies By: Katherine L. Milkman, Linnea Gandhi, Mitesh S. Patel, Heather N. Graci, Dena M. Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Jake Rothschild, Jonathan E. Bogard, Ilana Brody, Christopher F. Chabris, Edward Chang, Gretchen B. Chapman, Jennifer E. Dannals, Noah J. Goldstein, Amir Goren, Hal Hershfield, Alex Hirsch, Jillian Hmurovic, Samantha Horn, Dean Karlan, Ariella S. Kristal, Cait Lamberton, Michael N. Meyer, Allison H. Oakes, Maurice E. Schweitzer, Maheen Shermohammed, Jaochim H. Talloen, Caleb Warren, Ashley Whillans, Kuldeep N. Yadav, Julian J. Zlatev, Ron Berman, Chalanda N. Evans, Rahul Ladhania, Jens Ludwig, Nina Mazar, Sendhil Mullainathan, Christopher K. Snider, Jann Spiess, Eli Tsukayama, Lyle Ungar, Christophe Van den Bulte, Kevin G. Volpp and Angela L. Duckworth
  • Allocation of COVID-19 Relief Funding to Disproportionately Black Counties By: Pragya Kakani, Amitabh Chandra and Sendhil Mullainathan
  • Conversational Receptiveness: Expressing Engagement with Opposing Views By: M. Yeomans, J. Minson, H. Collins, H. Chen and F. Gino
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