Title: Using artificial intelligence techniques in collaborative filtering recommender systems: survey

Authors: Yousef Kilani; Bushra Alhijawi; Ayoub Alsarhan

Addresses: Department of Computer Information Systems, Faculty of Prince Al-Hussein B in Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan ' Department of Computer Information Systems, Faculty of Prince Al-Hussein B in Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan ' Department of Computer Information Systems, Faculty of Prince Al-Hussein B in Abdallah II for Information Technology, Hashemite University, Zarqa, Jordan

Abstract: The internet currently contains a huge data which is exponentially growing. This leads to the problem of information overload that makes the task of searching for information difficult and time consuming. Recommendation system is a filtering technique that recommends items to the users in order to reduce the list of choices and hence saves their times. The collaborative filtering recommendation algorithm is one of the most commonly used recommendation algorithms. There are many types of algorithm used to build the recommender systems, which include data mining techniques, information retrieval techniques and artificial intelligence algorithms. Although a number of studies have developed recommendation models using collaborative filtering, a few of them have tried to adopt both collaborative filtering and other artificial intelligence techniques, such as genetic algorithm, as a tool to improve recommendation results. This survey presents the state-of-the-art artificial intelligence techniques used to build the collaborative filtering recommender systems.

Keywords: collaborative filtering; recommender system; artificial intelligence; survey; genetic algorithms; recommender system survey; fuzzy logic; ant colony; swarm optimisation; neural network; machine learning.

DOI: 10.1504/IJAIP.2018.095491

International Journal of Advanced Intelligence Paradigms, 2018 Vol.11 No.3/4, pp.378 - 396

Received: 24 Oct 2016
Accepted: 19 Apr 2017
Published online: 08 Oct 2018 *

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