General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail: ijcte@iacsitp.com
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Editor-in-chief
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2020 Vol.12(1): 22-27 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2020.V12.1258

The Tourist Attractions Recommender System for Bangkok Thailand

Pasapitch Chujai, Jatsada Singthongchai, Surakirat Yasaga, Netirak Suratthara, and Khatthaliya Buranakutti

Abstract—The objective of this research is to design and develop a tool to evaluate tourists' satisfaction with the attractions recommendation system in Bangkok, Thailand. We have four main stages for the tourist attraction recommendation system. The first stage is to fill imputed missing values with association rules and multiple imputations. The second stage is constructing the tourist attractions recommendation model by ranking the tourist attractions with a ranking method and similarity measurements based on a personal recommender system with cosine algorithm. The third stage is to design and develop the personal recommender website. And the last stage is to evaluate the personal recommender system with four measurements: accuracy, precision, f-measure, and g-mean. The experiment results from a sampling of thirty people found that the tourist attraction recommendation system can: 1) make a positive recommendation 340 times, but 105 times will not meet the needs, and 2) make a negative recommendation 708 times, but 77 times will meet the needs. The results show that the tourist attractions recommendation system has satisfactory performance and reliability with high accuracy, precision, and f-measure, and g-mean values of 85.20%, 76.40%, 78.89%, and 84.26%, respectively. In addition, it was found that the users’ satisfaction towards the system was at a high level with a value of 4.60. This means that the proposed tourist attractions recommendation system can be used to recommend personal preferences as well.

Index Terms—Tourist attractions recommendation system, cosine similarity, association rule, imputed missing value.

P. Chujai, S. Yasaga, N. Suratthara, and K. Buranakutti are with the Electrical Technology Education Department, Faculty of Industrial Education and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand (e-mail: pasapitchchujai@gmail.com). J. Singthongchai is with the School of Information Science and Computer, Kalasin University, Thailand.

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Cite:Pasapitch Chujai, Jatsada Singthongchai, Surakirat Yasaga, Netirak Suratthara, and Khatthaliya Buranakutti, "The Tourist Attractions Recommender System for Bangkok Thailand," International Journal of Computer Theory and Engineering vol. 12, no. 1, pp. 22-27, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


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