Semantic Representation and Analysis of E-commerce Orders
University of Maryland
Date : 29/01/2018
E-commerce websites such as Amazon, Alibaba, and Walmart typically process billions of orders every year. Semantic representation and understanding of these orders is extremely critical for an eCommerce company. Each order can be represented as a tuple of <customer, product, price, date>. In this talk, I will describe two of our recent work (i) product embedding using MRNet-Product2Vec and (ii) generating fake orders using eCommerceGAN.
MRNet-Product2Vec [ECML-PKDD 2017]: In this work, we propose an approach called MRNet-Product2Vec for creating generic embeddings of products within an e-commerce ecosystem. We learn a dense and low-dimensional embedding where a diverse set of signals related to a product are explicitly injected into its representation. We train a Discriminative Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a product title fed through a Bidirectional RNN and at the output, product labels corresponding to fifteen different tasks are predicted.
eCommerceGAN: 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.
Arijit Biswas is currently a machine learning scientist at the India machine learning team in Amazon, Bangalore. His research interests are mainly in deep learning, machine learning and computer vision. Earlier he was a research scientist at Xerox Research Centre India (XRCI) from June, 2014 to July, 2016. He received his PhD in Computer Science from University of Maryland, College Park in April 2014. His PhD thesis was on Semi-supervised and Active Learning Methods for Image Clustering. His thesis advisor was David Jacobs and he closely collaborated with Devi Parikh and Peter Belhumeur during his stay at UMD. While doing his PhD, Arijit also did internships at Xerox PARC and Toyota Technological Institute at Chicago (TTIC). He has published papers in CVPR, ECCV, ACM-MM, BMVC, IJCV and CVIU. Arijit has a Bachelor's degree in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata. Arijit is also a recipient of the MIT Technology Review Innovators under 35 award from India in 2016.