Unveiling the landscape of recommendation systems: Evolution, algorithms, applications, and future prospects

Abstract

The purpose of this review paper is to explore the development history, core algorithms, application domains, and future trends of recommendation systems. In the era of information overload, recommendation systems are essential tools that have proven to be highly successful in diverse fields, such as e-commerce, social media, and movie recommendations. The paper examines various types of recommendation systems, including collaborative filtering, content filtering, and deep learning methods, analyzing their strengths and limitations. By delving into the intricate details of these systems, this study provides valuable insights into the advancements and challenges in recommendation technology. Understanding the evolution and capabilities of recommendation systems is crucial in harnessing their potential and improving user experiences in the dynamic digital landscape.