Personalized E-Commerce Recommendations Using Hybrider Recommender Systems

Abstract

In the rapidly evolving landscape of e-commerce, understanding customer preferences and product features is paramount for enhancing user experience and driving sales. This research presents a comprehensive exploration of hybrid recommender systems, which effectively combine various algorithms to provide personalized product recommendations. By integrating collaborative filtering and content-based filtering methods, we demonstrate how these systems can leverage user behavior and item characteristics to improve recommendation accuracy. The study highlights the challenges of combining different algorithms, emphasizing the importance of careful selection and adjustment to ensure optimal performance. Furthermore, we explore the role of smart technology in understanding customer desires through keyword analysis, moving beyond traditional purchase history methods. Our findings underscore the significance of user modeling processes in content-based filtering, which utilize diverse data-mining techniques to learn about customer preferences from online activities. This research contributes to the ongoing discourse on enhancing the transparency and effectiveness of hybrid recommender systems, addressing the complexities of user trust and data privacy in sensitive domains such as healthcare and finance. Ultimately, this work aims to provide valuable insights for researchers and practitioners seeking to optimize recommendation systems in e-commerce environments.