A Review of collaborative filtering Recommendation System
Sura I. Mohammed Ali1 and Sadiq Sahip Majeed2
Department of Mathematics and Computer Applications,
Collage of Science, Al-Muthanna University, Iraq
Emails: suraibraheem@mu.edu.iq
Corresponding Emails: 1suraibraheem@mu.edu.iq and 2sadiqss1983@mu.edu.iq
Received 26-04-2021, Accepted 10-05-2021, published 12-05-2021.
DOI: 10.52113/2/08.01.2021/120-131
AbstractKey wordsDOI
Recommended systems, also known as systems of recommendation, are a part of information
filtration systems which are utilized to predict the user’s estimation or choice for an object. In recent years,
recommended systems have been extensively used in e-commerce programs. Music, news, books, research
papers, and goods are likely to be the most popular E-commerce pages. This article provides an analysis of
the scope of recommendation systems and discusses recommended systems that include Collaborative
filtering (CF), one of the farthest common recommended methods, which are typically divided into three
major categories: Approaches to recommendation that are content-based, collective, or hybrid.
filtration systems which are utilized to predict the user’s estimation or choice for an object. In recent years,
recommended systems have been extensively used in e-commerce programs. Music, news, books, research
papers, and goods are likely to be the most popular E-commerce pages. This article provides an analysis of
the scope of recommendation systems and discusses recommended systems that include Collaborative
filtering (CF), one of the farthest common recommended methods, which are typically divided into three
major categories: Approaches to recommendation that are content-based, collective, or hybrid.
Recommender System, Collaborative Filtering, Content-based, Hybrid Recommendation
Approach.
Approach.