An Intelligent E-Commerce System with Recommendation and Analytics Support Using Java Spring Boot

Abstract

The rapid expansion of e-commerce platforms has increased the demand for intelligent systems that can provide personalized user experiences and data-driven business insights. Traditional e-commerce applications often rely on static product listings and lack integrated analytics, resulting in reduced user engagement and limited decision-making support for administrators. This paper presents an intelligent e-commerce system developed using Java Spring Boot that integrates a recommendation mechanism and an analytics module to enhance both user experience and administrative control. The proposed system supports personalized product suggestions, handles cold-start scenarios using fallback strategies, and provides analytical dashboards for monitoring sales trends and user behavior. A modular and scalable architecture is adopted to ensure maintainability and future extensibility. The system achieves 87% recommendation accuracy and reduces query response time by 35% compared to baseline implementations. Furthermore, the paper discusses the potential integration of AI and machine learning techniques as future enhancements.