Netflix实验发现推荐算法优化让中等热门内容更受欢迎，打破消费极化的固有认知。
Recommendation Quality and the Concentration of Consumption: Experimental Evidence from Netflix
- 通过850万用户实验，测试推荐系统改进对消费分布的影响
- 推荐效果提升使中等热门内容消费占比上升，超级热门内容被稀释
- 适合关注推荐系统如何影响内容生态的平台方与创作者
我们在Netflix上对850万用户进行实验，评估推荐技术改进如何影响消费产品集合。结果显示，推荐性能提升不仅增加了总消费量和用户对推荐的依赖，还使推荐和消费从最热门作品（‘超级明星’）向更多中等热度作品（‘中尾部’）扩散，对最冷门作品（‘长尾’）影响甚微。结果挑战了推荐系统加剧消费两极分化的观点，表明随着算法优化和平台扩张，投资中尾部内容的回报正在上升。
原文摘要 · Abstract (English)
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.
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