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).

翻译：个性化推荐系统在很大程度上塑造了用户的在线选择，但其针对性特征使得分离推荐价值与基础商品价值颇具挑战。我们构建了一个离散选择模型，该模型融合了推荐引发的效用、低秩异质性和灵活的状态依赖性，并将该模型应用于Netflix的收视数据。我们利用推荐算法引入的异质性变动来识别并分别评估这些组成部分，同时恢复无模型转移率以验证我们的结构模型。通过反事实分析，我们量化了个性化推荐带来的增量参与度。首先，我们发现用矩阵分解或基于流行度的算法替代当前推荐系统将分别导致参与度降低4%和12%，并降低消费多样性。其次，推荐带来的消费增长主要源于有效定向投放（有效定位），而非机械曝光（机械性曝光），其中中等流行度商品（相对于广泛受欢迎或非常小众的商品）的增益最大。