Transformer choice net: a transformer neural network for choice prediction
File(s)
Accepted version
Author(s)
Talluri, Kalyan
Wang, Hanzhao
Li, Xiaocheng
Type
Journal Article
Abstract
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 transformer
neural network architecture, the Transformer Choice Net, that is suitable for predicting multiple choices. Transformer networks turn out to be especially suitable for this task
as 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
neural network architecture, the Transformer Choice Net, that is suitable for predicting multiple choices. Transformer networks turn out to be especially suitable for this task
as 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
Date Acceptance
2026-04-04
Citation
INFORMS Journal on Data Science
Journal / Book Title
INFORMS Journal on Data Science
Copyright Statement
Copyright © 2026 Copyright Owner. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Accepted