Transformer Choice Net: A Transformer Neural Network for Choice Prediction

Discrete-choice models, such as multinomial logit, probit, or mixed logit, are widely used in marketing, economics, and operations research: given a set of alternatives, 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. Although 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 data sets, our architecture shows uniformly superior out-of-sample prediction performance compared with the benchmark models in the literature, without requiring any custom modeling for each instance.

History: Accepted for the Virtual Special Issue on GenAI, etc., for Business Analytics by Ningyuan Chen, senior editor.

Funding: The research of H. Wang was supported by the Emerging Scholar Research Fellowships, University of Sydney Business School.

Supplemental Material: The online appendix and code files are available at https://doi.org/10.1287/ijds.2025.0140.