Deep Learning for Choice Modeling
This addresses the challenge of inefficient choice modeling for fields like economics and marketing, though it appears incremental as it applies deep learning to an existing problem.
The paper tackles the problem of learning choice models from empirical data, which is often computationally intractable or sample inefficient, by developing deep learning-based models for feature-free and feature-based settings, and demonstrates their performance in synthetic and real data experiments.
Choice modeling has been a central topic in the study of individual preference or utility across many fields including economics, marketing, operations research, and psychology. While the vast majority of the literature on choice models has been devoted to the analytical properties that lead to managerial and policy-making insights, the existing methods to learn a choice model from empirical data are often either computationally intractable or sample inefficient. In this paper, we develop deep learning-based choice models under two settings of choice modeling: (i) feature-free and (ii) feature-based. Our model captures both the intrinsic utility for each candidate choice and the effect that the assortment has on the choice probability. Synthetic and real data experiments demonstrate the performances of proposed models in terms of the recovery of the existing choice models, sample complexity, assortment effect, architecture design, and model interpretation.