Title: Local search-based recommender system for computing the similarity matrix

Authors: Yousef Kilani; Ayoub Alsarhan; Mohammad Bsoul; Subhieh M. El-Salhi

Addresses: Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan

Abstract: Recommender systems reduce the users' effort in finding their favourite items among a great number of items. In collaborative-based RSs, there are different similarity measures like: genetic algorithms, Pearson and cosine-based similarity techniques. The number of items and personal attributes (e.g., environment, sex, job, religion, age, county, education, etc.) that are used by the similarity metric algorithms are increasing significantly which makes the recommendation task more difficult. In our project, we introduce a new RS that uses the local search algorithms to compute the similarity matrix. As far as we know, we have not found any work in the RS literature that uses local search algorithms techniques. We show that our new RS computes the similarity matrix and outperforms the other techniques like the Pearson correlation and cosine similarity and some of the recent genetic-based recommender systems.

Keywords: similarity matrix; recommender systems; local search algorithms; similarity measures; collaborative filtering-based recommender systems.

DOI: 10.1504/IJISTA.2019.100808

International Journal of Intelligent Systems Technologies and Applications, 2019 Vol.18 No.4, pp.391 - 404

Received: 06 Nov 2017
Accepted: 29 Mar 2018
Published online: 18 Jul 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article