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Recommendation Quality and the Concentration of Consumption: Experimental Evidence from Netflix

Author

Listed:
  • Guy Aridor
  • Winston Chou
  • Nathan Kallus
  • Antoine Scheid
  • Allen Tran
  • Kevin Zielnicki

Abstract

We study an experiment with 8.5 million users on Netflix's recommender system to measure how improvements in recommendation technology affect the set of products that get consumed. Improvements increase total consumption and users' reliance on recommendations while diffusing recommendations and consumption away from the most popular products ("superstars") toward a larger number of moderately popular products ("middle-tail"), with minimal effects on the most niche products ("long-tail"). Our results challenge the notion that recommender systems polarize consumption — raising the consumption shares of the head and tail at the expense of the middle — and suggest that the returns to investing in middle-tail products grow as algorithms improve and platforms scale.

Suggested Citation

  • Guy Aridor & Winston Chou & Nathan Kallus & Antoine Scheid & Allen Tran & Kevin Zielnicki, 2026. "Recommendation Quality and the Concentration of Consumption: Experimental Evidence from Netflix," CESifo Working Paper Series 12981, CESifo.
    • Handle: RePEc:ces:ceswps:_12981
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    Keywords

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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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