Review Paper on Recommendation Systems: Different Methods and Techniques

Abstract

The rapid growth of digital content has intensified the problem of information overload, making it challenging for users to access relevant resources. Recommender systems (RSs) address this issue by filtering data and providing suggestions, thereby improving decision-making and user satisfaction. This paper presents a comprehensive review of recommender systems (RSs), with particular emphasis on their methods, techniques, benefits, history, and applications. It examines traditional approaches, including collaborative filtering, content-based filtering, and hybrid strategies, before providing a classification of deep learning models in recommender systems and analyzing their impact on enhancing RS capabilities. In addition, the paper discusses evaluation methods used to assess recommendation performance and highlights their roles in measuring system effectiveness. Finally, it synthesizes the key challenges confronting recommender systems, including data sparsity, scalability, and cold-start issues.