Machine Learning Evaluation of Key Aspects of User Preferences and Usability of E-Commerce Websites

Abstract

This study explores the application of machine learning techniques to evaluate key aspects of user preferences and usability on e-commerce websites. As online shopping becomes increasingly prevalent, understanding user behavior and improving site usability are critical for enhancing customer satisfaction and driving sales. We employ various machine learning models to analyze user interaction data, including clickstreams, purchase history, and navigation patterns. The analysis focuses on identifying factors that influence user preferences, such as product recommendations, page load times, and user interface design. Additionally, usability metrics are assessed to determine their impact on the overall user experience. The findings highlight significant correlations between specific website features and user engagement levels, providing actionable insights for optimizing e-commerce platforms. By leveraging machine learning, this research offers a data-driven approach to enhancing user satisfaction and operational efficiency in the e-commerce industry.