Netflix研究推荐系统对用户观看时长的影响，发现个性化推荐显著提升参与度。
The Value of Personalized Recommendations: Evidence from Netflix
- 构建离散选择模型，分离推荐带来的价值与内容本身价值
- 替换为矩阵分解或流行度算法将导致参与度下降4%至12%
- 推荐主要提升中等热度内容的消费，而非单纯增加曝光
个性化推荐系统深刻影响用户在线选择，但其针对性使难以区分推荐本身与内容本身的贡献。本文构建一个包含推荐诱导效用、低秩异质性及灵活状态依赖的离散选择模型，并应用于Netflix的观看数据。通过利用推荐算法引入的独特个体差异，识别并分别评估各成分的价值，同时恢复无需模型假设的分流比率以验证结构模型。利用该模型评估反事实情景，量化个性化推荐带来的增量参与度。结果表明：若用矩阵分解或基于流行度的算法替代现有推荐系统，参与度将分别下降4%和12%，且消费多样性降低；推荐带来的大部分消费增长源于精准匹配，而非简单曝光，最大收益来自中等热度内容（非广受欢迎或极小众内容）。
原文摘要 · Abstract (English)
Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging. We build a discrete choice model that embeds recommendation-induced utility, low-rank heterogeneity, and flexible state dependence and apply the model to viewership data at Netflix. We exploit idiosyncratic variation introduced by the recommendation algorithm to identify and separately value these components as well as to recover model-free diversion ratios that we can use to validate our structural model. We use the model to evaluate counterfactuals that quantify the incremental engagement generated by personalized recommendations. First, we show that replacing the current recommender system with a matrix factorization or popularity-based algorithm would lead to 4% and 12% reduction in engagement, respectively, and decreased consumption diversity. Second, most of the consumption increase from recommendations comes from effective targeting, not mechanical exposure, with the largest gains for mid-popularity goods (as opposed to broadly appealing or very niche goods).
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