eCommerceGAN : A Generative Adversarial Network for E-commerce

TL;DR

This paper introduces eCommerceGAN, a GAN-based model that generates plausible e-commerce orders, helping to understand the full spectrum of potential transactions and relationships in online shopping ecosystems.

Contribution

The paper presents a novel GAN framework for modeling e-commerce orders, including a low-dimensional order representation and conditional generation for specific products.

Findings

ecGAN effectively generates plausible orders

ec^2GAN outperforms baseline models in order characterization

The approach aids in understanding potential e-commerce transactions

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 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.