Machine Learning Algorithms for Personalized Product Recommendations and Enhanced Customer Experience in E-Commerce Platforms

Abstract

The rapid expansion of e-commerce platforms has necessitated the deployment of advanced machine learning (ML) algorithms to deliver personalized product recommendations and enhance customer experience. These platforms generate vast amounts of user interaction data, creating opportunities to apply sophisticated computational models to predict user preferences and optimize the shopping journey. This paper explores the application of machine learning techniques in personalizing product recommendations and improving the customer experience on e-commerce platforms. Collaborative filtering, content-based filtering, and hybrid recommendation systems are evaluated for their effectiveness in leveraging user data to provide tailored suggestions.  Additionally, this study examines the integration of neural networks, such as deep learning approaches, in building scalable and dynamic recommendation systems. Reinforcement learning is discussed as a method to refine real-time recommendations by adapting to user behavior over time. Challenges such as data sparsity, cold start problems, and scalability are addressed, with solutions including the use of embeddings and transfer learning.  The role of explainability in machine learning is emphasized, highlighting its significance in fostering trust and transparency in recommendation engines. Finally, this paper investigates how these algorithms contribute to enhancing overall customer experience, from improving the discoverability of products to creating a seamless and engaging shopping experience.