Title: A fusion multi-criteria collaborative filtering algorithm for hotel recommendations

Authors: Qusai Y. Shambour; Mosleh M. Abualhaj; Qasem M. Kharma; Faris M. Taweel

Addresses: Faculty of Information Technology, Al-Ahliyya Amman University, P.O. Box 19328, Amman, Jordan ' Faculty of Information Technology, Al-Ahliyya Amman University, P.O. Box 19328, Amman, Jordan ' Faculty of Information Technology, Al-Ahliyya Amman University, P.O. Box 19328, Amman, Jordan ' Faculty of Information Technology, Al-Ahliyya Amman University, P.O. Box 19328, Amman, Jordan

Abstract: Recommender systems employ information filtering techniques to mitigate the problem of generating personalised recommendations in a digital world that is heavily overloaded with information. Recently, the tourism industry becomes more and more popular and the number of online hotel booking sites with search engines has been growing. However, using such sites can be time consuming and burdensome for potential travellers. Accordingly, this paper proposes a fusion multi-criteria user-item collaborative filtering (MC-UICF) recommendation algorithm that exploits the multi-criteria ratings of users and integrates MC user-based CF and MC item-based CF techniques to produce personalised hotel recommendations. Experimental results on two real-world multi-criteria datasets show the effectiveness of the proposed algorithm by outperforming other baseline single-criteria and multi-criteria CF recommendation approaches in terms of recommendation accuracy and coverage, in particular, when dealing with sparse datasets.

Keywords: information filtering; recommender systems; collaborative filtering; user-based CF; item-based CF; multi-criteria ratings; fusion recommendations; sparsity; hotel recommendations; recommendation accuracy.

DOI: 10.1504/IJCSM.2022.128653

International Journal of Computing Science and Mathematics, 2022 Vol.16 No.4, pp.399 - 410

Received: 22 Aug 2020
Accepted: 30 Sep 2020
Published online: 01 Feb 2023 *

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