eCommerceGAN : A Generative Adversarial Network for E-commerce

By Ashutosh Kumar, Arijit Biswas, Subhajit Sanyal. 2018

36 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Autoregressive Convolutional Neural Networks for Asynchronous Time Series (2017, methodology): “However, even regular LSTMs have been successfully applied with time/durations as explanatory variables (Żołna & Romański, 2017; Kumar et al., 2018).”
A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, background): “…social robot [189], cipher cracking [190], auxiliary automatic driving [236], continual learning [237], molecule development in oncology [238], GANs for finance [239-242], GANs for textile [243, 244], GANs for e-commerce [245], GANs for fluid-flow [246, 247 ], and in many others dominant areas.”