  E-commerce companies such as Amazon, Alibaba and Flipkart process billions of
orders every year. However, these orders represent only a small fraction of all
plausible orders. Exploring the space of all plausible orders could help us
better understand the relationships between the various entities in an
e-commerce ecosystem, namely the customers and the products they purchase. In
this paper, we propose a Generative Adversarial Network (GAN) for orders made
in e-commerce websites. Once trained, the generator in the GAN could generate
any number of plausible orders. Our contributions include: (a) creating a dense
and low-dimensional representation of e-commerce orders, (b) train an
ecommerceGAN (ecGAN) with real orders to show the feasibility of the proposed
paradigm, and (c) train an ecommerce-conditional-GAN (ec^2GAN) to generate the
plausible orders involving a particular product. We propose several qualitative
methods to evaluate ecGAN and demonstrate its effectiveness. The ec^2GAN is
used for various kinds of characterization of possible orders involving a
product that has just been introduced into the e-commerce system. The proposed
approach ec^2GAN performs significantly better than the baseline in most of the
scenarios.
