ISSN: 2222-6990
Open access
This work focuses on exploring the way to adopt Knowledge Graph technologies to innovate Tourism Recommendation Systems (TRS) for which the existing problem is on the aspects of data modularity and the capacity of algorithms. Using the conceptual framework of the Knowledge Graphs, complex relationships between the entities in the tourism domain can be better captured and used to improve the recommended actions’ accuracy and relevance. In this way, Knowledge Graphs facilitate semantic understanding and real-time data integration to adapt TRS to the specific users’ preferences and contextual characteristics. The paper aims at presenting different use cases of Knowledge Graphs in the context of TRS and the primary benefit they offer is the integration of different data sources to enhance the analysis process that helps travellers. Some major conclusions emphasize the role of Knowledge Graphs for extending TRS architecture, as well as analysing tendencies and further innovations to plans for recommendation in tourism. Finally, this research is valuable in the development of the theoretical foundation of TRS by assimilating recent IT advances to improve the level of satisfaction and quality of the travel experience.
Ahmad, I., Rodriguez, F., Kumar, T., Suomalainen, J., Jagatheesaperumal, S. K., Walter, S., ... & Harjula, E. (2024). Communications security in Industry X: A survey. IEEE Open Journal of the Communications Society.
https://ieeexplore.ieee.org/iel7/8782661/8901158/10409579.pdf
Berners-Lee, T., Hendler, J., & Lassila, O. (2023). The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. In Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide Web (pp. 91-
103).https://www.academia.edu/download/30225519/p01_thesemanticweb.pdf
Boutyline, A., & Soter, L. K. (2021). Cultural schemas: What they are, how to find them, and what to do once you’ve caught one. American Sociological Review, 86(4), 728-758. https://osf.io/ksf3v/download
Burhanuddin, N. A. N., Ahmad, N. A., Said, R. R., & Asimiran, S. (2021). Learning theories: Views from behaviourism theory and constructivism theory. International Journal of Academic Research in Progressive Education and Development, 10(1), 85-98.https://www.academia.edu/download/94677888/learning-theories-views-from-behaviourism-theory-and-constructivism-theory.pdf
Farmanbar, M., & Rong, C. (2020). Triangulum city dashboard: an interactive data analytic platform for visualizing smart city performance. Processes, 8(2), 250. https://www.mdpi.com/2227-9717/8/2/250/pdf
Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. applied sciences, 10(21), 7748. https://www.mdpi.com/2076-3417/10/21/7748/pdf
Fensel, A., Akbar, Z., Kärle, E., Blank, C., Pixner, P., & Gruber, A. (2020). Knowledge graphs for online marketing and sales of touristic services. Information, 11(5), 253. https://www.mdpi.com/2078-2489/11/5/253
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., ... & Li, Y. (2023). A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems, 1(1), 1-51.
https://dl.acm.org/doi/full/10.1145/3568022
Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403-
415.https://link.springer.com/content/pdf/10.1007/s12564-021-09716-7.pdf
Hermann, E. (2022). Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective. New media & society, 24(5), 1258-1277. https://www.ihp-microelectronics.com/php_scripts/publications/full_text_final_files/hermann-hermann_2021-2021.pdf
Júnior, P. S. S., Barcellos, M. P., de Almeida Falbo, R., & Almeida, J. P. A. (2021). From a scrum reference ontology to the integration of applications for data-driven software development. Information and Software Technology, 136, 106570. https://www.researchgate.net/profile/Paulo-Santos-Junior/publication/350158531_From_a_Scrum_Reference_Ontology_to_the_Integration_of_Applications_for_Data-Driven_Software_Development/links/64e369450acf2e2b520945fe/From-a-Scrum-Reference-Ontology-to-the-Integration-of-Applications-for-Data-Driven-Software-Development.pdf
Kejriwal, M. (2022). Knowledge graphs: A practical review of the research landscape. Information, 13(4), 161. https://www.mdpi.com/2078-2489/13/4/161
Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512-
520.https://www.sciencedirect.com/science/article/pii/S1877050921023929/pdf?md5=cac6c50b5769fee3890857460442d852&pid=1-s2.0-S1877050921023929-main.pdf
Nicholls, J., Kuppa, A., & Le-Khac, N. A. (2021). Financial cybercrime: A comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. Ieee Access, 9, 163965-163986. https://ieeexplore.ieee.org/iel7/6287639/6514899/09642993.pdf
Özdemir, M. A. (2022). Travellers’ Destination Attribute Preferences: A Choice-Based Conjoint (CBC) Analysis. Academica Turistica-Tourism and Innovation Journal, 15(2). https://link.springer.com/content/pdf/10.1007/s11257-023-09361-2.pdf
Paulino, I., Lozano, S., & Prats, L. (2021). Identifying tourism destinations from tourists’ travel patterns. Journal of Destination Marketing & Management, 19, 100508. https://dugi-doc.udg.edu/bitstream/handle/10256/18656/tipv_20200731.pdf?sequence=1#page=116
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.https://e-space.mmu.ac.uk/624632/3/2019-%20Computers%20and%20Education-%20Elsevier.pdf
Rahmadian, E., Feitosa, D., & Zwitter, A. (2022). A systematic literature review on the use of big data for sustainable tourism. Current Issues in Tourism, 25(11), 1711-1730. https://www.tandfonline.com/doi/pdf/10.1080/13683500.2021.1974358
Singh, C. K., Pavithra, N., & Joshi, R. (2022). Internet an integral part of human life in 21st century: a review. Current Journal of Applied Science and Technology, 41(36), 12-18. http://repository.stmscientificarchives.com/id/eprint/19/1/3963-Article%20Text-7267-2-10-20221015.pdf
Song, Y., & He, Y. (2023). Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm. Soft Computing, 27(24), 19159-19177. https://link.springer.com/article/10.1007/s00500-023-09330-2
Stewart, M. (2021). Understanding learning:: Theories and critique. In University teaching in focus (pp. 3-28). Routledge.https://www.researchgate.net/profile/Martyn-Stewart/publication/258441417_STEWART_M_2012_%27Understanding_learning_theories_and_critique%27_in_LHunt_and_D_Chalmers_eds_University_teaching_in_focus_a_learning-centred_approach_London_Routledge/links/5696676108aea2d7437467be/STEWART-M-2012-Understanding-learning-theories-and-critique-in-LHunt-and-D-Chalmers-eds-University-teaching-in-focus-a-learning-centred-approach-London-Routledge.pdf
Tavitiyaman, P., Qu, H., Tsang, W. S. L., & Lam, C. W. R. (2021). The influence of smart tourism applications on perceived destination image and behavioral intention: The moderating role of information search behavior. Journal of Hospitality and Tourism Management, 46, 476-487. https://nscpolteksby.ac.id/ebook/files/Ebook/Journal%20International/Jurnal%20Tourism/Journal%20of%20Hospitality%20and%20Tourism%20Management/Volume%2046%2C%20March%202021%2C%20Pages%20476-487.pdf
Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021). Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Communications Surveys & Tutorials, 23(2), 1342-1397. https://oulurepo.oulu.fi/bitstream/handle/10024/30908/nbnfi-fe2021090144887.pdf?sequence=1
Wang, X., Zhen, F., Tang, J., Shen, L., & Liu, D. (2022). Applications, experiences, and challenges of smart tourism development in China. Journal of Urban Technology, 29(4), 101-126. https://www.tandfonline.com/doi/abs/10.1080/10630732.2021.1879605
Weikum, G. (2021). Knowledge graphs 2021: A data odyssey. Proceedings of the VLDB Endowment, 14(12), 3233-
3238.https://pure.mpg.de/rest/items/item_3347360/component/file_3347361/content
Y?ld?z, E., Güngör ?en, C., & I??k, E. E. (2023). A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 571-596. https://www.mdpi.com/0718-1876/18/1/29/pdf
(Lei & Kamsin, 2024)
Lei, X., & Kamsin, A. (2024). Architecting Next-Generation Tourism Recommendation Systems: A Knowledge Graph Approach. International Journal of Academic Research in Business and Social Sciences, 14(7), 543–553.
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