Our goal is to study the behavioral process of a decision maker (DM) that leads to his choice.
To this end, we combine the established models of discrete choice with the recent algorithmic
advances in the emerging field of preference learning. Our proposed model takes the learning
information in form of the exemplary preference information, as revealed by a DM, and returns
the DM's choice probability. To accomplish our learning objective, we resort to the probabilistic
models of discrete choice and make use of the maximum likelihood inference. First experimental
results on suitable preference data suggest that our approach is not only intuitively appealing and
interesting from an interpretation point of view but also competitive to state-of-the-art preference
learning methods in terms of the prediction accuracy.