Keywords: E-commerce, Generative Adversarial Networks, Deep Learning, Order Embedding, Product Recommendation
TL;DR: In this paper, we propose a Generative Adversarial Network (GAN) for e-commerce to explore the space of all plausible orders
Abstract: 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 e-commerce 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 (ec2GAN) to generate the plausible orders involving a particular product. We evaluate ecGAN qualitatively to demonstrate its effectiveness. The ec2GAN is used for various kinds of characterization of possible orders involving cold-start products.