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Tourism profile measure for data-driven tourism segmentation

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Abstract

The digital revolution has brought about profound changes in research within the tourism segmentation field. The ease of grasping tourists’ behaviors is facilitated by the digital traces left on social networks. Existing studies focusing on tourists’ digital traces typically apply clustering algorithms to the tourism context. This paper introduces a novel measure, named tourism profile measure for determining tourism segmentation, also known as tourism profiling. The approach involves establishing a new clustering algorithm that centers on stays conducted by tourists, utilizing both the context and content of the trips. The proposed measure is then simulated and experimentally evaluated using a real dataset across various periods and diverse nationalities, particularly in the context of the French capital, Paris.

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Data availability

No datasets were generated or analysed during the current study.

Notes

  1. GADM: https://gadm.org/index.html.

  2. https://pro.visitparisregion.com/chiffres-du-tourisme/profil-clientele-tourisme.

  3. https://pro.visitparisregion.com/chiffres-du-tourisme/profil-clientele-tourisme.

  4. https://github.com/SmartGridandCity/TourismProfileMeasure.

  5. https://github.com/SmartGridandCity/TourismProfileMeasure.

References

  1. Hu F, Li Z, Yang C, Jiang Y (2019) A graph-based approach to detecting tourist movement patterns using social media data. Cartogr Geogr Inf Sci 46(4):368–382

    Article  Google Scholar 

  2. Zhang K, Chen D, Li C (2020) How are tourists different?—reading geo-tagged photos through a deep learning model. J Qual Assur Hosp Tour 21(2):234–243

    Article  Google Scholar 

  3. Alén E, Losada N, Domínguez T (2016) The impact of ageing on the tourism industry: An approach to the senior tourist profile. Soc Indic Res 127(1):303–322

    Article  Google Scholar 

  4. Rafael C, Almeida A (2017) Socio-demographic tourist profile and destination image in online environment. J Adv Manag Sci 5(5)

  5. Chareyron G, Da-Rugna J, Raimbault T (2014) Big data: a new challenge for tourism. In: 2014 IEEE international conference on big data (big data), pp 5–7. IEEE, New York

  6. Amaro S, Duarte P, Henriques C (2016) Travelers’ use of social media: a clustering approach. Ann Tour Res 59:1–15

    Article  Google Scholar 

  7. Godoy D, Amandi A (2005) User profiling in personal information agents: a survey. Knowl Eng Rev 20(4):329–361

    Article  Google Scholar 

  8. Eke CI, Norman AA, Shuib L, Nweke HF (2019) A survey of user profiling: state-of-the-art, challenges, and solutions. IEEE Access 7:144907–144924

    Article  Google Scholar 

  9. Araniti G, De Meo P, Iera A, Ursino D (2003) Adaptively controlling the QOS of multimedia wireless applications through" user profiling" techniques. IEEE J Sel Areas Commun 21(10):1546–1556

    Article  Google Scholar 

  10. Nilashi M, Ibrahim O, Ithnin N, Sarmin NH (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (em) and pca-anfis. Electron Commer Res Appl 14(6):542–562

    Article  Google Scholar 

  11. Cufoglu A (2014) User profiling-a short review. Int J Comput Appl 108(3)

  12. Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770

    Article  Google Scholar 

  13. Logesh R, Subramaniyaswamy V, Vijayakumar V, Li X (2019) Efficient user profiling based intelligent travel recommender system for individual and group of users. Mobile Netw Appl 24(3):1018–1033

    Article  Google Scholar 

  14. Refanidis I, Emmanouilidis C, Sakellariou I, Alexiadis A, Koutsiamanis R-A, Agnantis K, Tasidou A, Kokkoras F, Efraimidis PS (2014) myvisitplanner gr: Personalized itinerary planning system for tourism. In: Hellenic conference on artificial intelligence, pp 615–629. Springer, New York

  15. Abbasi-Moud Z, Vahdat-Nejad H, Sadri J (2021) Tourism recommendation system based on semantic clustering and sentiment analysis. Exp Syst Appl 167:114324

    Article  Google Scholar 

  16. Cao L, Luo J, Gallagher A, Jin X, Han J, Huang TS (2010) A worldwide tourism recommendation system based on geo tagged web photos. In: 2010 IEEE international conference on acoustics, speech and signal processing, pp 2274–2277. IEEE, New York

  17. Massimo D, Ricci F (2019) Clustering users’ pois visit trajectories for next-poi recommendation. In: Information and communication technologies in tourism 2019, pp 3–14. Springer, New York

  18. Rodríguez J, Semanjski I, Gautama S, Weghe N, Ochoa D (2018) Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data. Sensors 18(9):2972

    Article  Google Scholar 

  19. Wind YJ, Bell DR (2008) Market segmentation. Routledge, London

    Google Scholar 

  20. Calantone RJ, Johar JS (1984) Seasonal segmentation of the tourism market using a benefit segmentation framework. J Travel Res 23(2):14–24

    Article  Google Scholar 

  21. Dolnicar S (2020) Market segmentation for e-tourism. In: Handbook of e-tourism, pp 1–15

  22. Deseure-Charron F, Djebali S, Guérard G (2022) Clustering method for touristic photographic spots recommendation. In: Advanced data mining and applications: 18th international conference, ADMA 2022, Brisbane, Australia, November 28–30, 2022, Proceedings, Part II, pp 223–237. Springer, New York

  23. Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Ann Data Sci 2(2):165–193

    Article  Google Scholar 

  24. Backer E, Jain AK (1981) A clustering performance measure based on fuzzy set decomposition. IEEE Trans Pattern Anal Mach Intell 1:66–75

    Article  Google Scholar 

  25. Blanco-Moreno S, González-Fernández AM, Muñoz-Gallego PA (2023) Big data in tourism marketing: past research and future opportunities. Span J Market-ESIC (ahead-of-print)

  26. Dolnicar S (2002) A review of data-driven market segmentation in tourism. J Travel Tour Market 12(1):1–22

    Article  MathSciNet  Google Scholar 

  27. D’Urso P, De Giovanni L, Disegna M, Massari R, Vitale V (2021) A tourist segmentation based on motivation, satisfaction and prior knowledge with a socio-economic profiling: a clustering approach with mixed information. Soc Indic Res 154(1):335–360

    Article  Google Scholar 

  28. Gauch S, Speretta M, Chandramouli A, Micarelli A (2007) User profiles for personalized information access. In: The adaptive web, pp 54–89

  29. Wassler P, Nguyen THH, Schuckert M (2019) Social representations and resident attitudes: a multiple-mixed-method approach. Ann Tour Res 78:102740

    Article  Google Scholar 

  30. Chung MG, Herzberger A, Frank KA, Liu J (2020) International tourism dynamics in a globalized world: a social network analysis approach. J Travel Res 59(3):387–403

    Article  Google Scholar 

  31. Leal F, González–Vélez H, Malheiro B, Burguillo JC (2017) Semantic profiling and destination recommendation based on crowd-sourced tourist reviews. In: International symposium on distributed computing and artificial intelligence, pp 140–147. Springer, New York

  32. Amoretti M, Belli L, Zanichelli F (2017) Utravel: smart mobility with a novel user profiling and recommendation approach. Pervasive Mobile Comput 38:474–489

    Article  Google Scholar 

  33. McKercher B, Tolkach D, Eka Mahadewi NM, Byomantara DGN (2023) Choosing the optimal segmentation technique to understand tourist behaviour. J Vac Market 29(1):71–83

    Article  Google Scholar 

  34. D’urso P, Massari R (2019) Fuzzy clustering of mixed data. Inf Sci 505:513–534

    Article  MathSciNet  Google Scholar 

  35. Melnykov V, Maitra R (2010) Finite mixture models and model-based clustering. Stat Surv 4:80–116

    Article  MathSciNet  Google Scholar 

  36. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  37. Singh PK, Othman E, Ahmed R, Mahmood A, Dhahri H, Choudhury P (2021) Optimized recommendations by user profiling using a priori algorithm. Appl Soft Comput 106:107272

    Article  Google Scholar 

  38. Huang Z (1997) Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the 1st Pacific–Asia conference on knowledge discovery and data mining (PAKDD), pp 21–34. Citeseer

  39. Li C, Biswas G (2002) Unsupervised learning with mixed numeric and nominal data. IEEE Trans Knowl Data Eng 14(4):673–690

    Article  Google Scholar 

  40. Cheeseman PC, Stutz JC (1996) Bayesian classification (autoclass): theory and results. Adv Knowl Discov Data Min 180:153–180

    Google Scholar 

  41. Hsu C-C (2006) Generalizing self-organizing map for categorical data. IEEE Trans Neural Netw 17(2):294–304

    Article  Google Scholar 

  42. Ahmad A, Khan SS (2019) Survey of state-of-the-art mixed data clustering algorithms. IEEE Access 7:31883–31902

    Article  Google Scholar 

  43. Ping Y, Gao C, Liu T, Du X, Luo H, Jin D, Li Y (2021) User consumption intention prediction in Meituan. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 3472–3482

  44. Gössling S (2018) Tourism, tourist learning and sustainability: an exploratory discussion of complexities, problems and opportunities. J Sustain Tour 26(2):292–306

    Article  Google Scholar 

  45. Niaraki AS, Kim K (2009) Ontology based personalized route planning system using a multi-criteria decision making approach. Exp Syst Appl 36(2):2250–2259

    Article  Google Scholar 

  46. Cannas R (2012) An overview of tourism seasonality: key concepts and policies. Almatourism-J Tour Cult Territor Dev 3(5):40–58

    Google Scholar 

  47. Moreno A, Valls A, Isern D, Marin L, Borràs J (2013) Sigtur/e-destination: ontology-based personalized recommendation of tourism and leisure activities. Eng Appl Artif Intell 26(1):633–651

    Article  Google Scholar 

  48. Jia Z, Yang Y, Gao W, Chen X (2015) User-based collaborative filtering for tourist attraction recommendations. In: 2015 IEEE international conference on computational intelligence & communication technology, pp 22–25. IEEE, New York

  49. Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Exp Syst Appl 41(16):7370–7389

    Article  Google Scholar 

  50. Hakimi SL, Yau SS (1965) Distance matrix of a graph and its realizability. Q Appl Math 22(4):305–317

    Article  MathSciNet  Google Scholar 

  51. Struyf A, Hubert M, Rousseeuw P (1997) Clustering in an object-oriented environment. J Stat Softw 1(4):1–30

    Google Scholar 

  52. Müllner D (2011) Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378

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Author notes

  1. All authors contributed equally to this work.

Authors and Affiliations

  1. Léonard de Vinci Pôle Universitaire, Research Center, 92 916, Paris La Défense, France

    Guillaume Guerard, Quentin Gabot & Sonia Djebali

  2. LI-PARAD Laboratory EA 7432, Versailles University, 78035, Versailles, France

    Guillaume Guerard

Authors
  1. Guillaume Guerard
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  2. Quentin Gabot
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  3. Sonia Djebali
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Contributions

Conceived and designed the analysis: G.G. and S.D. Collected the data: S.D. Contributed data or analysis tools: G.G. and Q.G. Performed the analysis: G.G. and Q.G. Wrote the paper: G.G. Correction to reviewers: G.G.

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Correspondence to Guillaume Guerard.

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Guerard, G., Gabot, Q. & Djebali, S. Tourism profile measure for data-driven tourism segmentation. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02145-z

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