{"id":9798,"date":"2026-08-09T14:39:32","date_gmt":"2026-08-09T06:39:32","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9798"},"modified":"2026-08-09T16:04:30","modified_gmt":"2026-08-09T08:04:30","slug":"jase-202611-34-031","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-031","title":{"rendered":"Research on fuzzy clustering recommendation system for intelligent tourism management"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Luxi Chen1<\/sup> and Yanna Huang2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>School of History and Law, Yulin Normal University, Yulin, Guangxi 537000, China<\/p>\n\n\n\n

2<\/sup>Student Affairs Office, Yulin Normal University, Yulin, Guangxi 537000, China<\/p>\n<\/div>\n\n\n\n

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Received: May 09, 2026
Accepted: June 26, 2026
Publication Date: August 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Proposed Intelligent Fuzzy Clustering Framework for Tourism Data Analysis. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.031<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Tourism analytics is essential for understanding traveler behavior and supporting effective destination management. This study proposes a hybrid framework that combines deep learning, evolutionary optimization, and fuzzy clustering to improve tourism data segmentation. A realworld tourism dataset is preprocessed using Min-Max normalization and one-hot encoding. A Variational Autoencoder (VAE) is employed to extract compact latent features from high dimensional data, while Reinforced Evolution Strategy (RES) selects the most informative features by optimizing information gain and reconstruction quality. Fuzzy C-Means (FCM) clustering is then applied to categorize tourism records while handling uncertainty through soft cluster mem berships. The clustering performance is assessed using Partition Coefficient (PC), Partition Entropy (PE), and Xie-Beni Index (XB). Results demonstrate effective cluster formation, achieving a PC of 0.65, PE of 0.68, and clear cluster separation. The proposed framework also demonstrated strong destination retrieval and ranking capability, achieving Precision@10 of 0.89 , Recall@10 of 0.86 , F1-Score of 0.88 , MAP of 0.91 , and NDCG of 0.93. Compared to previous works, the model showed improved compactness and reduced ambiguity in cluster membership.<\/p>\n\n\n\n

Keywords: Tourism Recommendation, Variational Autoencoder, Reinforced Evolution Strategy, Fuzzy C-Means Clustering, Clustering Evaluation.<\/em><\/p>\n\n\n\n

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  1. [1] C. Moulin and P. Boniface, (2001) \u201cRouteing heritage for tourism: Making heritage and cultural tourism networks for socio-economic development\u201d International Journal of Heritage Studies 7(3): 237\u2013248. DOI: 10.1080\/13527250120079411.<\/li>\n
  2. [2] J. Borr\u00e0s, A. Moreno, and A. Valls, (2014) \u201cIntelligent tourism recommender systems: A survey\u201d Expert Systems with Applications 41(16): 7370\u20137389. DOI: 10.1016\/j.eswa.2014.06.007.<\/li>\n
  3. [3] D. Gavalas, C. Konstantopoulos, K. Mastakas, and G. Pantziou, (2014) \u201cMobile recommender systems in tourism\u201d Journal of Network and Computer Applications 39: 319\u2013333. DOI: 10.1016\/j.jnca.2013.04.006.<\/li>\n
  4. [4] B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, (2016) \u201cThe ethics of algorithms: Mapping the debate\u201d Big Data & Society 3(2): 2053951716679679. DOI: 10.1177\/2053951716679679.<\/li>\n
  5. [5] R. R. Yager, (2003) \u201cFuzzy logic methods in recommender systems\u201d Fuzzy Sets and Systems 136(2): 133\u2013149. DOI: 10.1016\/S0165-0114(02)00223-3.<\/li>\n
  6. [6] X. Yang, L. Zhang, and Z. Feng, (2024) \u201cPersonalized tourism recommendations and the e-tourism user experience\u201d Journal of Travel Research 63(5): 1183\u20131200. DOI: 10.1177\/00472875231187332.<\/li>\n
  7. [7] V. M. Charitopoulos, M. Rangoussi, and D. E. Koulouriotis, (2020) \u201cOn the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010\u20132018\u201d International Journal of Artificial Intelligence in Education 30(3): 371\u2013430. DOI: 10.1007\/s40593-020-00200-8.<\/li>\n
  8. [8] L. F. Chen and C. T. Tsai, (2016) \u201cData mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain\u201d Tourism Management 53: 197\u2013206. DOI: 10.1016\/j.tourman.2015.10.001.<\/li>\n
  9. [9] S. Rokhsaritalemi, A. Sadeghi-Niaraki, and S. M. Choi, (2023) \u201cExploring emotion analysis using artificial intelligence, GIS, and extended reality for urban services\u201d IEEE Access 11: 92478\u201392495. DOI: 10.1109\/ACCESS.2023.3307639.<\/li>\n
  10. [10] N. Stylos, J. Zwiegelaar, and D. Buhalis, (2021) \u201cBig data empowered agility for dynamic service industries: The tourism sector\u201d International Journal of Contemporary Hospitality Management 33(3): 1015\u20131036. DOI: 10.1108\/IJCHM-07-2020-0644.<\/li>\n
  11. [11] J. Cui, J. Cui, and Z. Liu, (2025) \u201cAn Algorithm for Travel Route Recommendation Integrating Fuzzy Clustering and Attribute-Based Features\u201d International Journal of High-Speed Electronics and Systems 35(3): 2540413. DOI: 10.1142\/S0129156425404139.<\/li>\n
  12. [12] J. Shen, C. Deng, and X. Gao, (2016) \u201cAttraction recommendation: Towards personalized tourism via collective intelligence\u201d Neurocomputing 173, Part 3: 789\u2013798. DOI: 10.1016\/j.neucom.2015.08.030.<\/li>\n
  13. [13] L. Yang, (2022) \u201cMulticriteria recommendation method of tourist routes based on tourist clustering\u201d Mobile Information Systems 2022: 9168899. DOI: 10.1155\/2022\/9168899.<\/li>\n
  14. [14] L. Huang, S. Chen, D. Shen, et al., (2023) \u201cA hybrid meta-heuristic algorithm with fuzzy clustering method for IoT smart electronic applications\u201d International Journal of Embedded Systems 16(1): 57\u201366. DOI: 10.1504\/IJES.2023.134120.<\/li>\n
  15. [15] S. P. R. Asaithambi, R. Venkatraman, and S. Venkatraman, (2023) \u201cA thematic travel recommendation system using an augmented big data analytical model\u201d Technologies 11(1): 28. DOI: 10.3390\/technologies11010028.<\/li>\n
  16. [16] P. D’Urso, L. De Giovanni, M. Disegna, et al., (2021) \u201cA Tourist Segmentation Based on Motivation, Satisfaction and Prior Knowledge with a Socio-Economic Profiling: A Clustering Approach with Mixed Information\u201d Social Indicators Research 154(1): 335\u2013360. DOI: 10.1007\/s11205-020-02537-y.<\/li>\n
  17. [17] N. Samani, S. Aliyari, and M. Jelokhani. \u201cDeveloping a group urban tourism recommendation system based on modified k-means and fuzzy best-worst method\u201d. Research Square, Preprint. 2025. DOI: 10.21203\/rs.3.rs-2500314\/v1.<\/li>\n
  18. [18] Tourism dataset. 2025. URL: https:\/\/www.kaggle.com\/code\/shroukelnagdy\/tourism-dataset\/input<\/a>.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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