Leveraging Machine Learning for Catalog Feed ...

Leveraging Machine Learning for Catalog Feed Optimization in E-commerce

Author Name : Varun Garg, Borada

ABSTRACT E-commerce platforms thrive on accurate, comprehensive, and optimized catalog feeds, which serve as the backbone for customer experiences and operational efficiency. However, the scale and complexity of modern ecommerce catalogs, coupled with frequent data inconsistencies and errors, present a significant challenge. This study explores the integration of machine learning (ML) techniques to optimize catalog feeds by improving data accuracy, completeness, and relevancy. We propose a framework that combines natural language processing (NLP), computer vision, and data clustering algorithms to automate tasks such as attribute extraction, category classification, and image quality enhancement. Through supervised and unsupervised learning models, the system identifies anomalies, fills data gaps, and ensures consistency across the catalog. Real-world application of this framework demonstrates its potential to reduce manual interventions, enhance product discoverability, and improve customer satisfaction.