Making sense of recommendations

2019/02/14 by Michael Yeomans, Anuj Shah, Sendhil Mullainathan +1 · 16 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence in Games #Recommender Systems and Techniques

paper · doi:10.1002/bdm.2118

openalex publication_date 2019/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

Citations

• Evaluating collaborative filtering recommender systems
• Individual differences in uses of humor and their relation to psychological well-being: Development of the Humor Styles Questionnaire
• The robust beauty of improper linear models in decision making.
• The base-rate fallacy in probability judgments
• Clinical versus mechanical prediction: A meta-analysis.
• Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
• Algorithm aversion: People erroneously avoid algorithms after seeing them err.

Cited by

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• Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?
• Understanding Consumer Preferences for Explanations Generated by XAI Algorithms
• Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty
• Decision authority and the returns to algorithms
• Relationship Between Trust in the AI Creator and Trust in AI Systems: The Crucial Role of AI Alignment and Steerability
• Bias in Adjudication and the Promise of AI: Challenges to Procedural Fairness
• Algorithmic media use and algorithm literacy: An integrative literature review
• In generative artificial intelligence we trust: unpacking determinants and outcomes for cognitive trust
• Human, Do You Think This Painting is the Work of a Real Artist?
• Cultural Differences in People's Reactions and Applications of Robots, Algorithms, and Artificial Intelligence
• Deploying Chatbots in Customer Service: Adoption Hurdles and Simple Remedies
• Algorithm aversion [wikipedia]

Related

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• Recommender Systems
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