AI RESEARCH
The Value of Personalized Recommendations: Evidence from Netflix
arXiv CS.LG
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ArXi:2511.07280v4 Announce Type: replace-cross 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