BibTex format
@article{Talluri:2026,
author = {Talluri, K and Wang, H and Li, X},
journal = {INFORMS Journal on Data Science},
title = {Transformer choice net: a transformer neural network for choice prediction},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit, are widely used in Marketing, Economics, and Operations Research: given a set of alter natives, the customer is modeled as choosing one of the alternatives to maximize a (latent) utility function. However, extending such models to situations where the customer chooses more than one item (such as in e-commerce shopping) has proven problematic. While one can construct reasonable models of the customer’s behavior, estimating such models becomes very challenging because of a combinatorial explosion in the number of possible subsets of items. In this paper we develop a transformerneural network architecture, the Transformer Choice Net, that is suitable for predicting multiple choices. Transformer networks turn out to be especially suitable for this taskas they take into account not only the features of the customer and the items but also the context, which in this case could be the assortment as well as the customer’s past choices. On a range of benchmark datasets, our architecture shows uniformly superior out-of-sample prediction performance compared to the benchmark models in the literature, without requiring any custom modeling for each instance.Key words: Generative AI, Transformer, Choice Modeling, Deep Learning
AU - Talluri,K
AU - Wang,H
AU - Li,X
PY - 2026///
TI - Transformer choice net: a transformer neural network for choice prediction
T2 - INFORMS Journal on Data Science
ER -