Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 17
DOI: https://doi.org/10.59646/819/07
Author: Dr. K. Loura Jency
Abstract
Artificial Intelligence (AI)-powered recommendation systems have become a fundamental component of modern e-commerce platforms, enabling businesses to deliver personalized shopping experiences and improve customer engagement. These systems analyze large volumes of user data, including browsing behavior, purchase history, product preferences, ratings, and interactions, to predict customer interests and recommend relevant products. This paper examines the role of AI in developing intelligent recommendation systems for e-commerce, focusing on techniques such as machine learning, deep learning, collaborative filtering, content-based filtering, and hybrid recommendation approaches. AI-driven recommendations can enhance customer satisfaction, increase conversion rates, improve product discovery, and support effective inventory and marketing strategies. However, challenges related to data privacy, scalability, recommendation bias, cold-start problems, and algorithmic transparency remain significant. The study highlights how advanced AI techniques can address these challenges and contribute to more adaptive, accurate, and efficient e-commerce recommendation platforms. The findings emphasize the potential of AI-powered recommendation systems to transform digital retail by providing personalized and data-driven decision-making for both customers and businesses.
Keywords: Artificial Intelligence, Recommendation Systems, E-Commerce, Machine Learning, Deep Learning, Personalization, Collaborative Filtering, Customer Behavior, Product Recommendation