# A Neural Network Based Choice Model for Assortment Optimization

arXiv: https://arxiv.org/abs/2308.05617 · HTML: https://mlsanity.com/paper/2308.05617/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 61 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [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)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Norm-Based Capacity Control in Neural Networks](https://mlsanity.com/paper/1503.00036/) (2015)
- [Size-Independent Sample Complexity of Neural Networks](https://mlsanity.com/paper/1712.06541/) (2017)
- [A vector-contraction inequality for Rademacher complexities](https://mlsanity.com/paper/1605.00251/) (2016)
- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study](https://mlsanity.com/paper/1712.06174/) (2017)
- [Exploring Alternatives to Softmax Function](https://mlsanity.com/paper/2011.11538/) (2020)
- [Discovering Context Effects from Raw Choice Data](https://mlsanity.com/paper/1902.03266/) (2019)
- [Representing Random Utility Choice Models with Neural Networks](https://mlsanity.com/paper/2207.12877/) (2022)
- [Decision Forest: A Nonparametric Approach to Modeling Irrational Choice](https://mlsanity.com/paper/1904.11532/) (2019)
- [Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance](https://mlsanity.com/paper/2109.12042/) (2021)
- [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)
- [Submodular Order Functions and Assortment Optimization](https://mlsanity.com/paper/2107.02743/) (2021)

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

- [Transformer Choice Net: A Transformer Neural Network for Choice Prediction](https://mlsanity.com/paper/2310.08716/) (2023)
- [From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems](https://mlsanity.com/paper/2507.10834/) (2025)
- [Diffusion-Based Data-Driven Assortment Optimization](https://mlsanity.com/paper/2608.11419/) (2026)

## Abstract (arXiv)

Discrete-choice models are used in economics, marketing and revenue management to predict customer purchase probabilities, say as a function of prices and other features of the offered assortment. While they have been shown to be expressive, capturing customer heterogeneity and behaviour, they are also hard to estimate, often based on many unobservables like utilities; and moreover, they still fail to capture many salient features of customer behaviour. A natural question then, given their success in other contexts, is if neural networks can eliminate the necessity of carefully building a context-dependent customer behaviour model and hand-coding and tuning the estimation. It is unclear however how one would incorporate assortment effects into such a neural network, and also how one would optimize the assortment with such a black-box generative model of choice probabilities. In this paper we investigate first whether a single neural network architecture can predict purchase probabilities for datasets from various contexts and generated under various models and assumptions. Next, we develop an assortment optimization formulation that is solvable by off-the-shelf integer programming solvers. We compare against a variety of benchmark discrete-choice models on simulated as well as real-world datasets, developing training tricks along the way to make the neural network prediction and subsequent optimization robust and comparable in performance to the alternates.