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A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model |
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| Author: | Forouzandeh, Saman1; Rostami, Mehrdad2; Berahmand, Kamal3 |
| Organizations: |
1School of Mathematics and Statistics, Faculty of Science, University of New South Wales, Sydney, NSW, Australia 2Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland 3School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 3.2 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022050533124 |
| Language: | English |
| Published: |
Informa,
2022
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| Publish Date: | 2022-05-05 |
| Description: |
AbstractRecommender systems have been pervasively applied as a technique of suggesting travel recommendations to tourists. Actually, recommendation systems significantly contribute to the decision-making process of tourists. A new approach of recommendation systems in the tourism industry by a combination of the Artificial Bee Colony (ABC) algorithm and Fuzzy TOPSIS is proposed in the present paper. A multi-criteria decision-making method called the Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been applied for the purpose of optimizing the system. Data were gathered through a 1015 online questionnaire on the Facebook social media site. In the first stage, the TOPSIS model defines a positive ideal solution in the form of a matrix with four columns, which indicates factors that get involved in this study. In the second stage, the ABC algorithm starts to search amongst destinations and recommends the best tourist spot to users. see all
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| Series: |
Fuzzy Information and Engineering |
| ISSN: | 1616-8658 |
| ISSN-E: | 1616-8666 |
| ISSN-L: | 1616-8658 |
| Volume: | 14 |
| Issue: | 1 |
| Pages: | 26 - 50 |
| DOI: | 10.1080/16168658.2021.2019430 |
| OADOI: | https://oadoi.org/10.1080/16168658.2021.2019430 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
113 Computer and information sciences |
| Subjects: | |
| Copyright information: |
© 2022 The Authors. Published by Taylor & Francis Group on behalf of the Fuzzy Information and Engineering Branch of the Operations Research Society, Guangdong Province Operations Research Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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https://creativecommons.org/licenses/by/4.0/ |