Filtering Techniques in Recommendation Systems: A Review

Abstract

Recommendation systems are not new to the world, they have rapidly become prevalent, appearing in almost every type of technology on a daily basis. As a result, recommendation systems were necessary to reduce the amount of time spent looking for the best and most essential items. Information filtering, user personalization, collaborative filtering, and hybrid filtering are just some of the ways used by recommendation systems in diversion, streaming, software, and other areas to present users and customers with customized content and products. The various filtering methods are compared and analyzed in order to improve the accuracy and quality of the recommendation system.