Local search-based recommender system for computing the similarity matrix Online publication date: Thu, 18-Jul-2019
by Yousef Kilani; Ayoub Alsarhan; Mohammad Bsoul; Subhieh M. El-Salhi
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 18, No. 4, 2019
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.
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