  Specifying utility functions is a key step towards applying the discrete
choice framework for understanding the behaviour processes that govern user
choices. However, identifying the utility function specifications that best
model and explain the observed choices can be a very challenging and
time-consuming task. This paper seeks to help modellers by leveraging the
Bayesian framework and the concept of automatic relevance determination (ARD),
in order to automatically determine an optimal utility function specification
from an exponentially large set of possible specifications in a purely
data-driven manner. Based on recent advances in approximate Bayesian inference,
a doubly stochastic variational inference is developed, which allows the
proposed DCM-ARD model to scale to very large and high-dimensional datasets.
Using semi-artificial choice data, the proposed approach is shown to very
accurately recover the true utility function specifications that govern the
observed choices. Moreover, when applied to real choice data, DCM-ARD is shown
to be able discover high quality specifications that can outperform previous
ones from the literature according to multiple criteria, thereby demonstrating
its practical applicability.
