Semantic Product Search for Matching Structured Product Catalogs in E-Commerce
This addresses product search challenges in e-commerce, but it is incremental as it builds on existing transformer methods for structured data.
The paper tackles the problem of retrieving semantically relevant products from structured and sparse e-commerce catalogs by proposing a new semantic product search algorithm that learns to represent and aggregate multi-instance fields using transformers. The results provide useful insights for improving product search, with detailed error analysis showing which query types benefit most from fielded representations and structured matching.
Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce. Compared to web documents, product catalogs are more structured and sparse due to multi-instance fields that encode heterogeneous aspects of products (e.g. brand name and product dimensions). In this paper, we propose a new semantic product search algorithm that learns to represent and aggregate multi-instance fields into a document representation using state of the art transformers as encoders. Our experiments investigate two aspects of the proposed approach: (1) effectiveness of field representations and structured matching; (2) effectiveness of adding lexical features to semantic search. After training our models using user click logs from a well-known E-commerce platform, we show that our results provide useful insights for improving product search. Lastly, we present a detailed error analysis to show which types of queries benefited the most by fielded representations and structured matching.