2009
NeurIPS
NeurIPS 2009
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics and Mathematics & Optimization
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Trend Setter
- Decision Making
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Hot Topic Early Bird
- preference learning
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Data Science & Analytics > Applications > Recommender Systems
Mathematics & Optimization > Optimization > Combinatorial Optimization
Machine Learning > Core Methods > Ranking
Machine Learning > Learning Types > Preference Learning
Artificial Intelligence > Core AI > Decision Making
Machine Learning > Learning Types > Decision Making