Improving recommendation quality and performance of genetic-based recommender system
by Bushra Alhijawi; Yousef Kilani; Ayoub Alsarhan
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 15, No. 1, 2020

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%.

Online publication date: Sat, 14-Dec-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com