A Data-Driven Approach to Modeling Choice

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Vivek Farias, Srikanth Jagabathula, Devavrat Shah


We visit the following fundamental problem: For a `generic model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? This problem is central to areas within operations research, marketing and econometrics. We present a framework to answer such questions and design a number of tractable algorithms (from a data and computational standpoint) for the same.