Recommendation Systems A Comprehensive Survey

To assist users in discovering goods, services, and content, recommendation systems have become a critical part of modern digital landscapes. The fundamental concepts, methods, challenges, and advancements in recommendation systems are discussed within this survey. We review a number of types of recommendation systems, including collaborative, content-based, and hybrid schemes. We also discuss the techniques that enable these systems, such as deep learning, matrix factorization, and graph-based methods. Industry applications, open problems, emerging trends, and key evaluation measures are all thoroughly examined.