Open AccessFiltering Techniques in Recommendation Systems: A Review
Author(s) -
S. Shargunam,
G. Rajakumar
Publication year - 2021
Publication title -
asian journal of science and applied technology
Language(s) - English
Resource type - Journals
ISSN - 2249-0698
DOI - 10.51983/ajsat-2021.10.2.3059
Subject(s) - collaborative filtering , recommender system , personalization , computer science , quality (philosophy) , order (exchange) , information filtering system , world wide web , information retrieval , software , multimedia , operating system , philosophy , epistemology , finance , economics
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.