E-Commerce Product Matching at Internet Scale
Muralidhar Sortur, Pawan Kumar Rajpoot, Manjunath Srinivas, Subhanandh, Hari Charan Rao · 2022
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 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. This paper proposes a combination of Deep Learning models integrated into the 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 have defined product data quality and measured it at every stage to improve the overall performance of Product Matching. This approach has yielded accurate product matching results at scale minimising false-positives