Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks

Conference paper


Characterising types of travellers can serve as a foundation for tourism recommender systems. This paper presents an approach to identify traveller types by analysing check-in data from location-based social networks. 33 million Foursquare check-ins from 266,909 users are segmented into 23,340 foreign trips based on traveller mobility patterns. Hierarchical clustering was then applied to identify distinct groups of trips by features such as travel duration, number of countries visited, radius of gyration, and the distance from home. The results revealed four clusters of trips, which manifest a novel grouping of people’s travel behaviour.


Data mining Cluster analysis Human mobility patterns Tourism Recommender systems 


  1. 1.
  2. 2.
    Dietz LW (2018) Data-driven destination recommender systems. In: Proceedings of the 26th conference on user modeling, adaptation and personalization, UMAP ’18, New York, NY, USA, July 2018. ACMGoogle Scholar
  3. 3.
    Herzog D, Wörndl, W (2014) A travel recommender system for combining multiple travel regions to a composite trip. CBRecSys@RecSys, vol 1245, pp 42–48, Foster City, CA, USAGoogle Scholar
  4. 4.
    Neidhardt J, Seyfang L, Schuster R, Werthner H (2014) A picture-based approach to recommender systems. J Inf Technol Tour 15(1):49–69CrossRefGoogle Scholar
  5. 5.
    Sertkan M, Neidhardt J, Werthner H (2017) Mapping of tourism destinations to travel behavioural patterns. In: Stangl B, Pesonen J (eds) Information and communication technologies in tourism, CH, December 2017. Springer, Cham, pp 422–434Google Scholar
  6. 6.
    Dietz LW, Herzog D, Wörndl W (2018) Deriving tourist mobility patterns from check-in data. In: Proceedings of the WSDM 2018 workshop on learning from user interactions, Los Angeles, CA, USA, February 2018Google Scholar
  7. 7.
    Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems. Expert Syst Appl 41(16):7370–7389CrossRefGoogle Scholar
  8. 8.
    Burke R, Ramezani M (2011) Matching recommendation technologies and domains. Springer, Boston, pp 367–386Google Scholar
  9. 9.
    Dietz LW, Weimert A (2018) Recommending crowdsourced trips. Vancouver, CanadaGoogle Scholar
  10. 10.
    Bhatia AK (1963) International tourism management. Sterling PublishersGoogle Scholar
  11. 11.
    Cohen E (1972) Towards a sociology of international tourism. Soc Res 39(1):164–182Google Scholar
  12. 12.
    Pearce PL (1982) The social psychology of tourist behavior. International Series in Experimental Social PsychologyGoogle Scholar
  13. 13.
    McKercher B (2002) Towards a classification of cultural tourists. Int J Tour Res 4(1):29–38CrossRefGoogle Scholar
  14. 14.
    Yiannakis A, Gibson H (1992) Roles tourists play. Ann Tour Res 19(2):287–303CrossRefGoogle Scholar
  15. 15.
    McCrae RR, John OP (1992) An introduction to the five-factor model and its applications. J Pers 60(2):175–215CrossRefGoogle Scholar
  16. 16.
    Gibson H, Yiannakis A (2002) Tourist roles: needs and the lifecourse. Ann Tour Res 29(2):358–383CrossRefGoogle Scholar
  17. 17.
    González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782CrossRefGoogle Scholar
  18. 18.
    Zhang Y, Wang L, Zhang YQ, Li X (2012) Towards a temporal network analysis of interactive WiFi users. Europhysics Lett 98(6)CrossRefGoogle Scholar
  19. 19.
    Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international world wide web conference, WWW’09, New York, NY, USA, April 2009. ACMGoogle Scholar
  20. 20.
    Hess A, Hummel KA, Gansterer WN, Haring G (2015) Data-driven human mobility modeling. ACM Comput Surv 48(3):1–39CrossRefGoogle Scholar
  21. 21.
    Song C, Koren T, Wang P, Barabási AL (2010) Modeling the scaling properties of human mobility. Nat Phys 6(10): 818–823CrossRefGoogle Scholar
  22. 22.
    Song C, Zehui Q, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021CrossRefGoogle Scholar
  23. 23.
    Ouyang X, Zhang C, Zhou P, Jiang H (2016) Deepspace: an online deep learning framework for mobile big data to understand human mobility patterns. CoRR,abs/1610.07009Google Scholar
  24. 24.
    Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in Foursquare. Media. AAAIGoogle Scholar
  25. 25.
    Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. Media. AAAIGoogle Scholar
  26. 26.
    Wang D, Pedreschi D, Song C, Giannotti F, Barabási AL (2011) Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’11, New York, NY, USA, August 2011. ACMGoogle Scholar
  27. 27.
    Jie Bao Y, Zheng DW, Mokbel M (2015) Recommendations in location-based social networks. GeoInformatica 19(3):525–565CrossRefGoogle Scholar
  28. 28.
    Zheng Y, Xie X (2011) Learning travel recommendations from user-generated GPS traces. ACM Trans Intell Syst Technol 2(1):1–29CrossRefGoogle Scholar
  29. 29.
    Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41(3):260–271CrossRefGoogle Scholar
  30. 30.
    Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems, SIGSPATIAL’12, New York, NY, USA, November 2012. ACM, pp 199–208Google Scholar
  31. 31.
    Hsieh HP, Li CT, Lin SD (2012) Exploiting large-scale check-in data to recommend time-sensitive routes. In: Proceedings of the ACM SIGKDD international workshop on urban computing, UrbComp ’12, New York, NY, USA, August 2012. ACM, pp 55–62Google Scholar
  32. 32.
    Kariryaa A, Johnson I, Schöning J, Hecht B (2018) Defining and predicting the localness of volunteered geographic information using ground truth data. In: Proceedings of the 2018 CHI conference on human factors in computing system, CHI’18, April 2018. ACM, p 265Google Scholar
  33. 33.
    Yang D, Zhang D, Bingqing Q (2016) Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans Intell Syst Technol 7(3):1–23CrossRefGoogle Scholar
  34. 34.
    Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  35. 35.
    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput Appl Math 20:53–65CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technical University of MunichDepartment of InformaticsGarchingGermany

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