# Deep Learning for Choice Modeling

arXiv: https://arxiv.org/abs/2208.09325 · HTML: https://mlsanity.com/paper/2208.09325/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability](https://mlsanity.com/paper/2002.00922/) (2020)
- [Representing Random Utility Choice Models with Neural Networks](https://mlsanity.com/paper/2207.12877/) (2022)
- [Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity](https://mlsanity.com/paper/1901.01030/) (2019)
- [Decision Forest: A Nonparametric Approach to Modeling Irrational Choice](https://mlsanity.com/paper/1904.11532/) (2019)
- [Online Learning and Profit Maximization from Revealed Preferences](https://mlsanity.com/paper/1407.7294/) (2014)
- [Learning from Stochastically Revealed Preference](https://mlsanity.com/paper/2206.01484/) (2022)
- [Parameter identification in Markov chain choice models](https://mlsanity.com/paper/1706.00729/) (2017)

## Built on by (most cited first, 3 of 3)

- [Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets](https://mlsanity.com/paper/2307.07090/) (2023)
- [Modeling Choice via Self-Attention](https://mlsanity.com/paper/2311.07607/) (2023)
- [Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks](https://mlsanity.com/paper/2603.24705/) (2026)

## Abstract (arXiv)

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.