Cloud-Powered Personalization: An Advanced Recommendation System for E-Commerce Sites

This research study aims to enhance personalized recommendation systems by integrating cloud computing technology. The goal is to improve the precision and efficacy of recommendation algorithms by utilizing cloud infrastructure's scalability, flexibility, and computational capacity. The study covers the state of recommendation systems today, highlighting the challenges traditional models face in managing large datasets and adapting to changing user preferences. It also explores the design and workings of cloud-powered recommendation systems, emphasizing the benefits of shifting processing and storage to cloud-based infrastructure. The study also explores the use of machine learning algorithms in the cloud environment, highlighting how the cloud infrastructure supports real-time processing and ongoing learning, allowing recommendation systems to quickly adjust to changing user behavior. The study also investigates the consequences of integrating social interactions, user-generated content, and contextual data into the recommendation system. It also discusses security and privacy issues, suggesting anonymization methods, access restrictions, and encryption techniques to protect user privacy without compromising system functionality. The study presents experimental results comparing the performance of the proposed cloud-powered recommendation system against conventional on-premises models, assessing measures like response time, scalability, and accuracy.