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

2026-08-21

AI Summary

研究通过改进Netflix推荐系统，增加总消费量并分散对最流行产品的依赖，转而更多消费中等流行产品，挑战了推荐系统极化消费的观点。

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 titles (``superstars") toward a larger number of moderately popular titles (``middle-tail"), with minimal effects on the most niche titles (``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.

Problem

• recommendation technology
• consumption
• popular titles
• middle-tail
• long-tail

Innovation

• Recommender System
• Consumption Diffusion
• Algorithm Improvement