Improving recommendation quality and performance of genetic-based recommender system. (7th December 2019)

• Record Type: Journal Article
• Authors: Alhijawi, Bushra; Kilani, Yousef; Alsarhan, Ayoub
• Is Part Of: International journal of advanced intelligence paradigms. Volume 15:Number 1(2020)
• Page Start: 77
• Page End: 88
• Publication Date: 2019-12-07
• Subjects: collaborative filtering -- recommender system -- genetic algorithms -- similarity; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

The recommender system came to help the user in finding the required item in a short time by filtering the available choices. This paper addresses the problem of recommending items to users by presenting new three genetic-based recommender system ( GARS +, GARS ++ and HGARS ). HGARS is a combination of GARS + with GARS ++. It is an enhanced version of the genetic-based recommender system that works without the being a hybrid model. In the proposed algorithms, the genetic algorithm is used to find the optimal similarity function. This function depends on a liner combination of values and weights. We experimentally prove that HGARS improves the accuracy by 16.1%, the recommendation quality by 17.2% and the performance by 40%.