Towards a simplified ontology for better e-commerce search

Abstract

Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations of bag-of-words methods but requires an ontology. We show that current ontologies are not optimized for search and propose a simplified ontology framework designed specifically for e-commerce search and retrieval. We also present three methods for automatically extracting product classes for the proposed ontology and compare their performance relative to each other.

References in corpus (1)

• Distributed Representations of Words and Phrases and their Compositionality

Cited by in corpus (4)

• End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings
• Online Similarity Learning with Feedback for Invoice Line Item Matching
• Ranking sentences from product description & bullets for better search
• Active Learning for Product Type Ontology Enhancement in E-commerce