A Comprehensive Survey of Recommendation Methods and Techniques

With a substantial increase in digital information on various platform browsing applications, recommendation systems are imperative to filter large amounts of information in more palatable ways through the organization of the most relevant topics. This can be applied to various domains, including e-commerce, education, books, movies, music, and more. As such, it is crucial to have a thorough understanding of the various generations of recommendation systems. In this study, we conduct a comprehensive examination of generations of recommendation systems using various applicable techniques. To examine the objective of recommendation systems, we introduce challenges to recommendation systems. The generations are then broken down into sub-classes for further examination. These are content-based, collaborative filtering, hybrid models, matrix factorization, web usage mining, personality-based models, collaborative filtering using deep learning techniques, deep content-based models, and combined modeling of users and items using reviews. First-generation techniques often employ rudimentary approaches, whereas second- and third-generation techniques utilize more complex models that delve deeper into the vast amount of available data. Based on this analysis, we believe that our survey enhances the understanding of generational recommendation system models and highlights the importance of selecting suitable techniques to design novel, cross-disciplinary models.