Muralidhar Sortur

Product Matching In E-Commerce, Product Matching is one of the fundamental problems for various use cases like  (1) Competitive pricing of products,  (2) Deduplication of products in the catalog,  (3) Grouping items from various merchants  (4) Recommending products. The requirement is to match a product accurately against a catalog spread across tens of thousands of taxonomy nodes and millions of items. Product matching results must be accurate, and the margin for error is minimal to use Product Matching across use cases.  In this talk, we touch upon the “Six honest serving-men” by Rudyard Kipling –What, Why, How, Where, When, and Who of Product Matching. We share the overall approach, experience in designing and orchestrating Deep Learning models with a scalable architecture to achieve the required results. Here we have approached the problem at the grass-root level consisting of five stages  (1) Identifying attributes per taxonomy node  (2) Classification of Products  (3) Attribute Enrichment from NER (Text) and Image feature extraction  (4) Search against multiple indices and filter results for mandatory attributes  (5) Re-rank to improve the relevancy of shortlisted results.  We also address Product data quality, measured at every stage to improve the overall performance of Product Matching, towards meeting business needs.