Understanding and Evaluating Multi-class Product Classification Methods for E-commerce

Abstract

In e-commerce, enhancing Natural Language Processing (NLP) models\u27 understanding of search queries can significantly improve product relevance and overall user experience. Even with advancements in the search space, being able to accurately classify items for shopping queries remains challenging due to noisy data, ambiguous user intent, and the wide range of products available. This research aims to explore different strategies implementing and improving queryproduct classification. The methodology involves a comparative assessment of various model performances for multi-class product classification, data augmentation techniques for handling class-imbalances, and the design of the User Interface (UI) of a Human-In-The-Loop (HITL) Machine Learning (ML) system. The hope is that this approach will lead to enhancements in query-product matching, with direct implications for better search results and product recommendation