Hybrid System Architecture for High Performance of E-Commerce Recommendation Systems

Abstract

This study presents a hybrid system architecture for high-performance cloud-based e-commerce recommendation systems that integrate user data, product data, rating data, and machine learning algorithms to enhance the accuracy of recommendations.  The architecture consists of many modules to provide efficient data processing and personalized suggestions. These modules include user administration, admin control, data pretreatment, and machine learning recommendation creation.  The system's prediction accuracy and user engagement are both enhanced by the combination of hybrid models, content-based filtering, and collaborative filtering.  The integration of machine learning with cloud computing ensures scalability, flexibility, and exceptional accuracy, making this technology appropriate for modern e-commerce systems.