Machine Learning Algorithms for Optimizing Search Personalization and Site Reliability in E-Commerce Platforms A Comparative Analysis of Linear Regression, SVR, and AdaBoost
Divya Soundarapandian
Abstract
As e-commerce platforms become increasingly embedded in daily life, a pivotal enabler of personalized user experiences, shaping both customer engagement and business success. This review examines the multifaceted applications of AI-driven personalization in digital environments, with particular attention to search optimization and site reliability engineering. It explores how AI systems leverage large-scale data analytics to identify intricate patterns in consumer behavior, enabling the delivery of tailored recommendations that enhance user satisfaction and retention. The integration of deep learning models, including auto-encoder networks, further improves semantic understanding and recommendation accuracy. The findings underscore that an effective AI personalization infrastructure transcends technical implementation—it is integral to achieving brand differentiation and sustainable competitive advantage. Successful deployment requires robust data architectures, efficient AI model management, and close collaboration between engineering, marketing, and data governance teams to ensure ethical and responsible personalization practices. Additionally, the study investigates key challenges in personalized search systems, web service reliability prediction, and the complexity of implementing adaptive learning mechanisms in educational contexts. Broader implications are also explored, particularly regarding algorithmic filtering, information diversity, and personalization in political communication. Overall, this comprehensive review bridges the gap between the theoretical foundations and practical applications of AI-driven personalization, offering valuable insights into its potential, limitations, and future directions across e-commerce and digital platform ecosystems.