eCommerceGAN: A Generative Adversarial Network for e-commerce

Ashutosh Kumar, Arijit Biswas, Subhajit Sanyal

Feb 11, 2018 (modified: Feb 12, 2018) ICLR 2018 Workshop Submission readers: everyone
  • 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.
  • TL;DR: In this paper, we propose a Generative Adversarial Network (GAN) for e-commerce to explore the space of all plausible orders
  • Keywords: E-commerce, Generative Adversarial Networks, Deep Learning, Order Embedding, Product Recommendation
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