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This is a Literature Review study (also classified as: Quantitative Study).

September 26, 2025Open Access

Machine Learning Algorithm Applications in E- Commerce Platforms

YWYuying WangRutgers, The State University of New Jersey

Key Points

Machine learning significantly improves customer satisfaction, guiding business decisions and enhancing operational productivity.
The analysis highlights applications like recommendation systems and predictive analytics, which offer competitive advantages in e-commerce platforms.
Through literature review and real-life analyses, key benefits of machine learning include scalability and continuous improvement over time.
Challenges such as data quality, algorithmic bias, and privacy issues are thoroughly discussed, emphasizing the need for governance in ML applications.

Abstract

The internet and e-commerce have been in existence for several decades, while machine learning (ML), a subfield of artificial intelligence, is a relatively recent yet highly influential development. Moreover, it is widely used to offer highly relevant products and services, optimize prices, uncover fraudulent activities, and improve customer trust. It contributes to evidence-based decision-making, operational productivity, and enhanced end-user satisfaction. This paper investigates the key applications of ML in e-commerce, focusing on recommendation systems, predictive analytics, dynamic pricing, and customer service automation. From reviewing the current literature and analyzing the industrys real-life use scenarios, the main benefits of ML are summarized, including scalability, competitive advantage, and continuous improvement over time. Issues such as data quality, algorithmic bias, privacy, and governance are also discussed in depth. The results suggest that ML can significantly change digital commerce strategies, guide informed business decisions, and maximize customer satisfaction in a global e-commerce setting.

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