arXiv:2511.07280v5 Announce Type: replace-cross Abstract: 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
The proliferation of personalized recommendation systems across digital platforms necessitates rigorous economic analysis to quantify their true value and impact on consumer choice, which this research addresses.
Understanding the economic value of personalized recommendations is crucial for businesses optimizing their algorithms and for regulators assessing market power and consumer welfare in the digital economy.
This research provides a more robust methodology for disentangling the value generated by recommendation algorithms from the intrinsic value of the underlying content or goods, refining how we assess personalized experiences.
- · Companies with advanced recommendation engines
- · Data scientists and economists in AI research
- · Consumers benefiting from personalized discovery
- · Companies with generic or poorly performing recommendation systems
Improved understanding of how AI-driven personalization influences user behavior and economic outcomes.
Increased investment in and competition for developing more effective and ethically sound recommendation algorithms.
Potential for regulatory frameworks to evolve based on quantifiable impacts of personalized algorithmic influence on markets and individuals.
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Read at arXiv cs.LG