A Data-Driven Approach to Modeling Choice
Abstract
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.
Cite
Text
Farias et al. "A Data-Driven Approach to Modeling Choice." Neural Information Processing Systems, 2009.Markdown
[Farias et al. "A Data-Driven Approach to Modeling Choice." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/farias2009neurips-datadriven/)BibTeX
@inproceedings{farias2009neurips-datadriven,
title = {{A Data-Driven Approach to Modeling Choice}},
author = {Farias, Vivek and Jagabathula, Srikanth and Shah, Devavrat},
booktitle = {Neural Information Processing Systems},
year = {2009},
pages = {504-512},
url = {https://mlanthology.org/neurips/2009/farias2009neurips-datadriven/}
}