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arXiv · 2008.08180

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

Jason Ingyu Choi·Surya Kallumadi·Bhaskar Mitra·Eugene Agichtein·Faizan Javed
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Abstract

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.

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Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra, Eugene Agichtein, Faizan Javed. 2020-08-18. Semantic Product Search for Matching Structured Product Catalogs in E-Commerce. https://arxiv.org/abs/2008.08180

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