Skip to main content

Web Usage Mining in Tourism — A Query Term Analysis and Clustering Approach

  • Conference paper
Information and Communication Technologies in Tourism 2010
  • 2942 Accesses

Abstract

According to current research, one of the most promising applications for web usage mining (WUM) is in identifying homogenous user subgroups (Liu, 2008). This paper presents a prototypical workflow and tools for analyzing user sessions to extract business intelligence hidden in web log data. By considering a leading Swedish destination gateway, we demonstrate how query term analysis in combination with session clustering can be utilized to effectively explore the information needs of website users. The system thus overcomes many of the limitations of typical web site analysis tools that only offer general statistics and ignore the opportunities offered by unsupervised learning techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Biswas, A. & Krishan R. (2004). The Internet’s impact on marketing, Journal of Business Research, 57 (7): 681–684.

    Article  Google Scholar 

  • Bhatnagar, A. & Ghose, S. (2004). Segmenting consumers based on the benefits and risks of Internet shopping, Journal of Marketing Research, 40 (2): 235–243.

    Google Scholar 

  • Cho, V. & Leung, P. (2002). Towards using knowledge discovery techniques in database marketing for the tourism industry, Journal of Quality Assurance in Hospitality & Tourism, 3(3): 109–131.

    Article  Google Scholar 

  • Dias, J. G. & Vermunt J. K. (2007). Latent class modeling of website users’ search patterns: Implications for online market segmentation, Journal of Retailing and Consumer Services, (14): 359–368.

    Article  Google Scholar 

  • Hastie, T., Tibshirani, R. & Friedman, J. (2009). The elements of statistical learning — Data mining, inference and prediction (2nd ed.), New York, Springer.

    Google Scholar 

  • Honda, T., Yamamoto, M. & Ohuchi, A. (2006). Automatic Classification of Websites based on Keyword Extraction of Nouns, In, Hitz, M., Sigala, M., Murphy, J. Eds.), Information and Communication Technologies in Tourism 2006 NY: Springer: 263–272

    Chapter  Google Scholar 

  • Jolliffe, I. T., (2002). Principal Component Analysis. Springer-Verlag. USA.

    Google Scholar 

  • Larose, D.T. (2005). Discovering knowledge in data — An introduction to data mining. John Wiley & Sons, New Jersey.

    Google Scholar 

  • Liu, B. (2008). Web Data Mining — Exploring hyperlinks, contents and usage data (2nd ed.), Springer, New York.

    Google Scholar 

  • Mobasher, B. (2008). Web Usage Mining, In: Liu, B. (ed.) Web Data Mining-Exploring hyperlinks, contents and usage data (2nd ed.) Springer, New York, pp 449–483.

    Google Scholar 

  • Murphy, J., Hofacker, C.F., & Bennett, M. (2001). Website-generated Market-Research data: Tracing the tracks left behind by visitors, Cornell Hotel and Restaurant Administration Quarterly, 42(1): 82–91.

    Google Scholar 

  • Olmeda, I. & Sheldon, P.J. (2002). Data Mining Techniques and Applications for Tourism Internet Marketing, Journal of Travel & Tourism Marketing, 11(2/3): 1–20.

    Article  Google Scholar 

  • Orlando, S. & Silvestri, F. (2009) Query Log Analysis for Enhancing Web Search, IEEE/WIC/ACM International Conference on Web Intelligence, Milano, Italy.

    Google Scholar 

  • Pelleg, D., Moore, A.W. (2000). X-means: Extending K-means with Efficient Estimation of the Number of clusters, Proceedings of the Seventeenth International Conference on Machine Learning. pp 727–734. USA.

    Google Scholar 

  • Pyle, D. (1999). Data preparation for data mining. New York, Morgan Kaufmann Publishers.

    Google Scholar 

  • Scharl, A., Wöber, K. & Bauer, Ch. (2004). An integrated approach to measure web site effectiveness in the European hotel industry, Journal of Information Technology and Tourism, 6(4): 257–271

    Article  Google Scholar 

  • Schegg, R., Steiner, Th., Gherissi-Labben, T. & Murphy, J. (2005). Using Log-File Analysis and Website Assessment to Improve Hospitality Websites, Frew, A.. (Ed.) Information and Communication Technologies in Tourism 2005. New York, Springer, 566–576.

    Chapter  Google Scholar 

  • Tichler, G., Grossman, W. & Werthner, H. (1999). Using Data Mining in Analysing Local Tourism Patterns. In Buhalis, D. & Schertler, W. Eds., Information and Communication Technologies in Tourism 1999 Vienna: Springer: 1–11.

    Google Scholar 

  • Werthner, H. & Ricci, F. (2004). E-commerce and tourism. Communications of the ACM, 47(12): 101–105.

    Article  Google Scholar 

  • Wolk, A. & Wöber, K. (2009). A Comprehensive Study of Info Needs of City Travellers in Europe, Journal of Information Technology and Tourism, 10(2): 119–131.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag/Wien

About this paper

Cite this paper

Pitman, A., Zanker, M., Fuchs, M., Lexhagen, M. (2010). Web Usage Mining in Tourism — A Query Term Analysis and Clustering Approach. In: Gretzel, U., Law, R., Fuchs, M. (eds) Information and Communication Technologies in Tourism 2010. Springer, Vienna. https://doi.org/10.1007/978-3-211-99407-8_33

Download citation

Publish with us

Policies and ethics