Transformer Choice Net: A Transformer Neural Network for Choice Prediction

• URL: http://arxiv.org/abs/2310.08716v1
• Date: Thu, 12 Oct 2023 20:54:10 GMT
• Title: Transformer Choice Net: A Transformer Neural Network for Choice Prediction
• Authors: Hanzhao Wang, Xiaocheng Li, Kalyan Talluri
• Abstract summary: We develop a 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. Our architecture shows uniformly superior out-of-sample prediction performance compared to the leading models in the literature.
• Score: 6.6543199581017625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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. While one can construct reasonable models of the customer's behavior, estimating such models becomes very challenging because of the 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 leading models in the literature, without requiring any custom modeling or tuning for each instance.

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