Vijay Gabale – Taking Fashion and Lifestyle Commerce Towards SKUs Using Deep Image and Text Parsing
Channel: Hasgeek TV
Date: 29/07/2016

In this talk, I will describe challenges, insights, innovations and experiences in building a large-scale deep learning system to prepare SKUs (Stock Keeping Units) for millions of fashion products.

E-commerce is booming across the globe at an astonishing rate. India alone is expected to witness CAGR of 50% by 2020. In such a fast-faced and mobile-first market, online commerce experiences (e.g., intent identification, search results, product recommendations) are steadily replacing deep discounts as the means to acquire and retain consumers. While consumers interact with buyer-side portals, the organization of products (aka catalogues) acquired through seller-side platforms plays pivotal role towards search, discovery and personalization experiences. Unfortunately, different sellers have different interpretations of the same product and distributed onboarding of products produces poorly organized e-commerce catalogues. For instance, a large fraction of products in such catalogues have inconsistency between product title and product image, missing product titles or keywords, incorrect tagging of keywords and several duplicate products. This further results in poor and irrelevant search, personalization and recommendations; degrading user experience significantly.

With the use of SKUs, normalizing and cleaning of products has been possible for products in consumer electronics space. However, due to poorly organized catalogues and inherent difficulties in describing and quantifying product details, the problem of organizing products as SKUs in categories such as fashion (e.g., fashion apparel, fashion accessories) and lifestyle (such as home-decor) has been largely unsolved. The problem becomes even more critical if one has to build an aggregate fashion commerce application that ingests several such poorly tagged catalogues. In this talk, I will describe how deep learning has made it possible to prepare SKUs for fashion and lifestyle products. We innovate and apply deep image parsing to extract detailed product information from product images. We further apply deep learning models originally conceived for images to process text paragraphs to solve Named Entity Recognition and Disambiguation to produce structured outputs. Using confidence scores of the two process, we then combine results of text and image parsing to merge and create unique products. I will also describe evaluation criteria and several engineering-challenges to build large-scale systems to process product steams and normalize millions of fashion products. I will especially give insights into experiences and experimentation on the amount and quality of labeled data needed to achieve desired accuracy.