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|a Farias, Vivek F.
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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|a Sloan School of Management
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|a Farias, Vivek F.
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|a Jagabathula, Srikanth
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|a Shah, Devavrat
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|a Jagabathula, Srikanth
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|a Shah, Devavrat
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|a A Data-Driven Approach to Modeling Choice
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|b Neural Information Processing Systems Foundation,
|c 2015-02-20T15:15:23Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/95439
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|a 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.
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|a National Science Foundation (U.S.) (CAREER CNS 0546590)
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|a en_US
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|a Article
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|t Advances in Neural Information Processing Systems (NIPS) 22
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