Recent Recommender Systems and Analysis of the Machine Learning Public Instances

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (ML) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in ML public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and ML techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99^%) and the error rate occurs at (5%, 25^%) for the ML public instances.