Local search-based recommender system for computing the similarity matrix. (3rd June 2019)

• Record Type: Journal Article
• Authors: Kilani, Yousef; Alsarhan, Ayoub; Bsoul, Mohammad; El-Salhi, Subhieh M.
• Is Part Of: International journal of intelligent systems technologies and applications. Volume 18:Number 4(2019)
• Page Start: 391
• Page End: 404
• Publication Date: 2019-06-03
• Subjects: similarity matrix -- recommender systems -- local search algorithms -- similarity measures -- collaborative filtering-based recommender systems; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 006.3
• Languages: English
• ISSNs: 1740-8865

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.