  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.
