Travellers and Their Joint Characteristics Within the Seven-Factor Model

  • Julia Neidhardt
  • Hannes Werthner
Conference paper


Recommender systems face specific challenges in the travel domain, as the tourism product is typically very complex. In addition, travelling can be seen as an emotional experience. Thus travel decisions are usually not only based on rational criteria but are rather implicitly given. Therefore sophisticated user models are required. In this paper it is analysed in detail whether the seven-factor model is capable of differentiating between different groups of users in an accurate way. Within this model each user is described with respect to seven travel behavioural patterns that account for both tourist roles and personality traits of a user. To identify groups of travellers, individual attributes are used and also a cluster analysis is conducted. With the help of statistical analyses clear evidence is provided that the seven-factor model is capable of distinguishing between different groups of users in a meaningful and effective way.


User modelling Personality-based recommender systems Cluster analysis 


  1. Braunhofer, M., Elahi, M., Ge, M., & Ricci, F. (2014). Context dependent preference acquisition with personality-based active learning in mobile recommender systems. International Conference on Learning and Collaboration Technologies (pp. 105–116). Springer International Publishing.Google Scholar
  2. Cohen, E. (1972). Toward a sociology of international tourism. Social Research, 164–182.Google Scholar
  3. Cohen, E. (1974). Who is a tourist? A conceptual clarification. The Sociological Review, 22(4), 527–555.CrossRefGoogle Scholar
  4. Delic, A., Neidhardt, J., & Werthner, H. (2016). Are sun lovers nervous?. e-Review of Tourism Research (eRTR), ENTER 2016 Special issue.Google Scholar
  5. European Travel Commission, (2013). New media trend watch. Retrieved 9, 2016 from
  6. Fesenmaier, D., Kuflik, T., & Neidhardt, J. (2016). RecTour 2016: Workshop on recommenders in tourism. In M. A. Boston (Eds.), Proceedings of the 10th ACM Conference on Recommender systems (RecSys 2016). USA: ACM.Google Scholar
  7. Gibson, H., & Yiannakis, A. (2002). Tourist roles: Needs and the lifecourse. Annals of Tourism Research, 29(2), 358–383.CrossRefGoogle Scholar
  8. Goldberg, L. R. (1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59(6), 1216.CrossRefGoogle Scholar
  9. Gretzel, U., Mitsche, N., Hwang, Y. H., & Fesenmaier, D. R. (2004). Tell me who you are and i will tell you where to go: Use of travel personalities in destination recommendation systems. Information Technology & Tourism, 7(1), 3–12.CrossRefGoogle Scholar
  10. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.Google Scholar
  11. Jani, D. (2014). Relating travel personality to Big Five factors of personality. Turizam: Znanstveno-stručni časopis, 62(4), 347–359.Google Scholar
  12. Le Roux, B., & Rouanet, H. (2004). Geometric data analysis: From correspondence analysis to structured data analysis. Springer Science & Business Media.Google Scholar
  13. Marsland, S. (2014). Machine learning: An algorithmic perspective. CRC press.Google Scholar
  14. Neidhardt, J., Schuster, R., Seyfang, L., & Werthner, H. (2014). Eliciting the users’ unknown preferences. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 309–312). ACM.Google Scholar
  15. Neidhardt, J., Seyfang, L., Schuster, R., & Werthner, H. (2015). A picture-based approach to recommender systems. Information Technology & Tourism, 15(1), 49–69.CrossRefGoogle Scholar
  16. Pearce, P. L. (1982). The social psychology of tourist behaviour: International series in experimental social psychology. Pergamon Press.Google Scholar
  17. Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. Recommender Systems Handbook (pp. 1–34). USA: Springer.Google Scholar
  18. Tkalcic, M., & Chen, L. (2015). Personality and recommender systems. Recommender systems handbook (pp. 715–739). US: Springer.Google Scholar
  19. Werthner, H., Alzua-Sorzabal, A., Cantoni, L., Dickinger, A., Gretzel, U., Jannach, D., et al. (2015). Future research issues in it and tourism. Information Technology & Tourism, 15(1), 1–15.CrossRefGoogle Scholar
  20. Werthner, H., & Klein, S. (1999). Information technology and Tourism—A challenging relationship. Wien—New York: Springer.CrossRefGoogle Scholar
  21. Woszczynski, A., Roth, P., & Segars, A. (2002). Exploring the theoretical foundations of playfulness in computer interactions. Computers in Human Behavior, 18(4), 369–388.CrossRefGoogle Scholar
  22. Yfantidou, G. (2008). Tourist roles, gender and age in greece: A study of tourists in Greece Georgia Yfantidou, George Costa, Maria Michalopoulos. International Journal of Sport Management, Recreation & Tourism, 1(1), 14–30.CrossRefGoogle Scholar
  23. Yiannakis, A., & Gibson, H. (1992). Roles tourists play. Annals of Tourism Research, 19(2), 287–303.CrossRefGoogle Scholar
  24. Zins, A. H. (2007). Exploring travel information search behavior beyond common frontiers. Information Technology & Tourism, 9(3–1), 149–164.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.E-Commerce Group TU WienViennaAustria

Personalised recommendations