Enhancing Customer Experience through AI-Powered Personalization: A Data Science Perspective in E-Commerce

The intent of this research is to explore the application of advanced machine learning technologies to improve personalization within the e-commerce field. As the number of online shopping platforms is growing rapidly, the ability to provide the users with personalized inferences and optimal experiences is becoming crucial. By applying artificial intelligence and machine learning technologies, such as deep learning and reinforcement learning, the present research explores the creation of an intelligent recommendation system that can understand the preferences and behavior of an individual. The process of building different machine learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and Deep Reinforcement Learning (DRL), will be analyzed to investigate their capability to predict the future responses. Therefore, the study aims to define the criteria for evaluating their performance in terms of optimized decision-making and personalization within online shopping environments. Additional goals of the research include the investigation of the effect of feature engineering and preprocessing on their performance and the study of the implications and best strategies related to their deployment within the actual environments of e-commerce. By attaining these goals, the present study seeks to provide the reader with valuable insights and recommendations symbiotic for these types of business.