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.

本文提出了一种适用于预测多个选择的 Transformer Choice Net 转换神经网络架构，在一系列基准数据集上，该架构相对于文献中的主流模型展现出了一致的优秀的预测性能，而不需要对每个实例进行定制建模或调整。