AI-Powered E-Commerce Personalization and Recommendation Systems

Abstract

The rapid growth of e-commerce has necessitated advanced AI-driven solutions to enhance user experience through personalization and recommendation systems. This research explores the application of Deep Reinforcement Learning (DRL) as a cutting-edge AI method for optimizing e-commerce recommendations, dynamically adapting to user behavior in real time. Additionally, TensorFlow Recommenders (TFRS), an advanced AI tool, is leveraged to build scalable and efficient personalized recommendation models. By integrating DRL and TFRS, this research demonstrates how AI can improve customer engagement, increase conversion rates, and drive business growth in the digital marketplace. The findings highlight the potential of AI in transforming e-commerce personalization with intelligent, data-driven recommendations.