Machine Learning Applications in Smart Tourism: Overview, Research Challenges, and the Road Ahead

Authors

Keywords:

Analysis, Artificial Intelligence, Machine learning, Smart tourism, Review

Abstract

Tourism plays a vital role in stimulating economic growth, creating employment opportunities and facilitating cultural exchange and mutual understanding between people of different backgrounds. However, as an industry facing increasing competition on a global scale, tourism needs to embrace technologies to adapt quickly to changes. The emergence of smart tourism offers a route to achieve a competitive edge. To this end, machine learning techniques are used to analyze the abundant data available, forecast trends and and provide best solutions. This paper provides an overview, challenges and future directions of research on machine learning techniques applied in smart tourism.

References

[1] Hui Ma. “Development of a smart tourism service system based on the Internet of Things and machine learning”. In: Journal of Supercomputing 80 (5 Mar. 2024), pp. 6725–6745. issn: 15730484. doi: 10 . 1007 / s11227-023-05719-w.

[2] Ton Jaitrong and Aziz Nanthaamornphong. “Classification of Tourism-Related Topics in Phuket: A Case Study”. In: 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT NCON). 2022, pp. 218–222. doi: 10.1109/ECTIDAMTNCON53731. 2022.9720391.

[3] Khan Md Hasib, Nurul Akter Towhid, and Md Golam Rabiul Alam. “Topic Modeling and Sentiment Analysis using Online Reviews for Bangladesh Airlines”. In: Institute of Electrical and Electronics Engineers Inc., 2021, pp. 428–434. isbn: 9781665400664. doi: 10 1109/IEMCON53756.2021.9623155.

[4] Yu Hsiang Hsiao and Pei Yi Hung. “A Method of Evaluating User Preference Similarity for Destination Recommendations Based on Online Review Analysis”. In: Institute of Electrical and Electronics Engineers Inc., 2023, pp. 452–455. isbn: 9798350324228. doi: 10.1109/IIAI-AAI59060.2023.00093.

[5] Junna Hou. “Service Quality Evaluation Algorithm of Tourist Scenic Spots Based on Fuzzy Cluster Analysis”. In: 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC). 2022, pp. 1–6. doi: 10 . 1109 / ICMNWC56175 . 2022 . 10031898.

[6] Hui Cui et al. “Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning”. In: Institute of Electrical and Electronics Engineers Inc., 2023, pp. 653–657. isbn: 9781665460033. doi: 10.1109/ITNEC56291.2023.10082614.

[7] Khurniawan Eko Saputro, Sri Suning Kusumawardani, and Silmi Fauziati. “Development of semi-supervised named entity recognition to discover new tourism places”. In: 2016 2nd International Conference on Science and Technology-Computer (ICST). 2016, pp. 124–128. doi: 10.1109/ICSTC.2016.7877360.

[8] Jui Hung Chang et al. “Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan”. In: vol. 2018-January. Institute of Electrical and Electronics Engineers Inc., July 2017, pp. 132–137. isbn: 9780769563282. doi: 10.1109/SC2.2017.27.

[9] Houria Laaroussi, Fatima Guerouate, and Mohamed Sbihi. “Deep Learning Framework for Forecasting Tourism Demand”. In: Institute of Electrical and Electronics Engineers Inc., Nov. 2020. isbn: 9781728159508. doi: 10.1109 / ICTMOD49425 .2020.9380612.

[10] Kamuna Kipa et al. “A Data-Driven Approach to Predict Social Impact of Rural Tourism: Insights from UNWTO’s Video Campaigns”. In: 2023 IEEE International Conference on Big Data (BigData). 2023, pp. 6201–6203. doi: 10 . 1109 / BigData59044 . 2023 . 10386389.

[11] Muhammad Ilham Mubarak and Z. K. A. Baizal. “Tourism Route Recommendation Using Reinforcement Learning”. In: 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). 2023, pp. 1–7. doi: 10.1109/I2CT57861.2023.10126347.

[12] Matan Mor, Sagi Dalyot, and Yael Ram. “Who is a tourist? Classifying international urban tourists using machine learning”. In: Tourism Management 95 (Apr.2023). issn: 02615177. doi: 10.1016/j.tourman.2022. 104689.

[13] Mehrbakhsh Nilashi et al. “A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques”. In: Computers and Industrial Engineering 109 (July 2017), pp. 357–368. issn: 03608352. doi: 10.1016/j.cie.2017.05.016.

[14] Lochana Vishwajith et al. “Machine Learning Technique based Trip Planning System - TripMa”. In: 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). 2022, pp. 1362–1369. doi: 10.1109/ICOSEC54921.2022.9952110.

[15] Zhenzhen Yu and Shan Wang. “Smart Tourism Route Planning System Based on Machine Learning Algorithm”. In: 2022 International Conference on Computers and Artificial Intelligence Technologies (CAIT). 2022, pp. 16–20. doi: 10 . 1109 / CAIT56099 . 2022 . 10072232.

[16] Shilin Xu, Yang Liu, and Chun Jin. “Forecasting daily tourism demand with multiple factors”. In: Annals of Tourism Research 103 (Nov. 2023). issn: 01607383. doi: 10.1016/j.annals.2023.103675.

[17] Gang Xie, Yatong Qian, and Shouyang Wang. “Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach”. In: Tourism Management 82 (Feb. 2021). issn: 02615177. doi: 10.1016/j.tourman.2020.104208.

[18] Rodrigo Mesa-Arango et al. “Missions and factors determining the demand for affordable mass space tourism in the United States: A machine learning approach”. In: Acta Astronautica 204 (Mar. 2023), pp. 307–320. issn: 00945765. doi: 10 .1016 / j .actaastro.2023.01.006.

[19] Toshima Makoondlall-Chadee et al. “Sustainable tourism, Technology and Internet 4.0: Opportunities and Challenges for Small Island Developing States”. In: 2021 IoT Vertical and Topical Summit for Tourism. 2021, pp. 1–4. doi: 10.1109/IEEECONF49204.2021.9604866.

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Published

01-08-2024

How to Cite

[1]
P. Premananthan, H. Le, M.-H. T. Nguyen, M. Fahim, and T. Q. Duong, “Machine Learning Applications in Smart Tourism: Overview, Research Challenges, and the Road Ahead”, EAI Endorsed Tour Tech Intel, vol. 1, no. 1, Aug. 2024.

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Vol. 1 No. 1 (2024): EAI Endorsed Transactions on Tourism, Technology and Intelligence

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Articles

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Copyright (c) 2024 Premisha Premananthan, Hang Le, Minh-Hien T. Nguyen, Trung Q. Duong, M. Fahim

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