# Enhancing Discrete Choice Models with Representation Learning

## TL;DR

This paper introduces a novel approach to discrete choice models that combines knowledge-driven and data-driven representations, using neural networks to improve prediction accuracy while maintaining interpretability.

## Contribution

It proposes a hybrid modeling framework that integrates neural network representations into traditional discrete choice models, enhancing their predictive power and parameter estimation.

## Key findings

- L-MNL and L-NL models outperform traditional models in predictive accuracy.
- The new models improve parameter estimation accuracy.
- Source code is publicly available for reproducibility.

## Abstract

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.