A Systematic Study of Machine Learning Applications in E-Commerce UX Personalization

This study provides a systematic analysis of the role of machine learning in personalizing e-commerce user experiences (UX). Through a review of selected recent literature, the study explores various machine learning techniques applied to e-commerce, including supervised learning, unsupervised learning, deep learning, and ensemble methods. The research discusses these approaches' effectiveness in enhancing UX through personalized recommendations, search results, dynamic pricing models, and virtual try -on experiences. The analysis examines four main areas: recommendation systems for improved product discovery and user engagement, dynamic pricing optimization for maximizing revenue and customer satisfaction, virtual try-on technologies for enhancing fashion retail experiences, and personalized search. The study investigates how these technologies contribute to creating engaging and efficient shopping experiences while examining implementation challenges such as data privacy and algorithmic bias. Additionally, the research discusses emerging trends, technological advancements, and future research directions to further advance personalization in e-commerce. Overall, this research contributes to the understanding of machine learning in e-commerce personalization for both researchers and practitioners.