Software Engineering Strategies for Real-Time Personalization in E-Commerce Recommendations

Abstract

This chapter addresses the cold start problem, a significant challenge in e-commerce recommendation systems, through an innovative software engineering approach. Focused on personalized user engagement, the system employs a sophisticated collaborative filtering model strategically integrated within a robust software architecture. A key software engineering facet involves differentiating new and existing users using machine learning algorithms that scrutinize individual shopping behaviors. Leveraging collaborative filtering principles, the model intelligently analyzes similar users' purchasing patterns, ensuring a dynamic recommendation engine. The software engineering-driven integration supports accuracy and responsiveness, showcasing the transformative potential of adept software engineering strategies in revolutionizing personalized recommendations for e-commerce platforms.