Recommendation System: A transformative Artificial Intelligence Tool for E-commerce

An Artificial Intelligence tool known as a recommendation system is quickly changing e-commerce space. It is bringing radical changes in customer experience, and customer engagement. Recommendation systems are built using sophisticated machine learning and artificial intelligence algorithms. They evaluate large datasets in order to predict customers preferences and behaviors and provide valuable insight to the e-commerce platform which them to provide pe4rsonalised product recommendations to their customers that are accurate and customized. The transformational influence of various AI-based techniques, used in recommendation system such as collaborative filtering, content-based filtering, and hybrid approaches, are investigated in this study. This study illustrates the revolutionary potential of recommendation systems by examining previous research work and articles and demonstrating how they might improve customer satisfaction, retention, and income production. However, amidst their significant benefits, the study also focuses on the challenges for the recommendation systems such as data privacy concerns, algorithmic biases, and the cold-start problem. By addressing these challenges and delving into ethical considerations, this research provides insights that contribute to the continued advancement and responsible deployment of Recommendation Systems in e-commerce. The study ends with insights into future prospects for research and development in this area.