Automated Assignment of Hotel Descriptions to Travel Behavioural Patterns

  • Lisa GlatzerEmail author
  • Julia Neidhardt
  • Hannes Werthner
Conference paper


The amount of people using online platforms to book a travel accommodation has grown tremendously. Hence, tour operators implement recommender systems to offer most suitable hotels to their customers. In this paper, a method of using hotel descriptions for recommendation is introduced. Different natural language processing methods were applied to pre-process a corpus of hotel descriptions. Further, three machine learning approaches for the allocation of hotel descriptions to travel behavioural patterns were implemented: clustering, classification and a dictionary-based approach. The main results show that clustering cannot be used in this context since the algorithm mostly relies on the operator-dependent structure of the descriptions. Supervised classification achieves the highest precision for six travel patterns, whereas the dictionary approach works best for one pattern. In general, the results for the different travel patterns vary due to the unequally distributed data sets as well as various characteristics of the patterns.


User modelling Personality-based recommender systems Hotel descriptions Text mining Machine learning Seven-factor-model 


  1. Aggarwal, C.C., Zhai, C.: A survey of text clustering algorithms. In: Mining Text Data, pp. 77–128. Springer, New York (2012)Google Scholar
  2. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Inc. (2009)Google Scholar
  3. Burke, R., Ramezani, M.: Matching recommendation technologies and domains. In: Recommender Systems Handbook, pp. 367–386. Springer (2011)Google Scholar
  4. Cohen, E.: Toward a sociology of international tourism. Soc. Res. 39(1), 164–182 (1972)Google Scholar
  5. Cosh, K.: Text mining Wikipedia to discover alternative destinations. In: The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 43–48 (2013)Google Scholar
  6. Gibson, H., Yiannakis, A.: Tourist roles: needs and the lifecourse. Ann. Tourism Res. 29(2), 358–383 (2002)CrossRefGoogle Scholar
  7. Goldberg, L.R.: The structure of phenotypic personality traits. Am. Psychol. 48(1), 26–34 (1999)CrossRefGoogle Scholar
  8. Gupta, G.K.: Introduction to Data Mining with Case Studies. Prentice-Hall of India Pvt. Ltd. (2006)Google Scholar
  9. Hippner, H., Rentzmann, R.: Text mining. Informatik-Spektrum 29(4), 287–290 (2006)CrossRefGoogle Scholar
  10. Johansson, V.: Lexical diversity and lexical density in speech and writing: a developmental perspective. Working Papers of Department of Linguistics and Phonetics, Lund University 53, 61–79 (2008)Google Scholar
  11. Lahlou, F.Z., Mountassir, A., Benbrahim, H., Kassou, I.: A text classification based method for context extraction from online reviews. In: 8th International Conference on Intelligent Systems: Theories and Applications (SITA) (2013)Google Scholar
  12. Leskovec, J., Rajaraman, A., Ullman J.: Mining of Massive Datasets, 2nd edn. Cambridge University Press (2014)Google Scholar
  13. Neidhardt, J., Werthner, H.: Travellers and their joint characteristics within the seven-factor model. In: Information and Communication Technologies in Tourism 2017, pp. 503–515. Springer (2017)Google Scholar
  14. Neidhardt, J., Schuster, R., Seyfang, L., Werthner, H.: Eliciting the users’ unknown preferences. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 309–312. ACM (2014)Google Scholar
  15. Neidhardt, J., Seyfang, L., Schuster, R., Werthner, H.: A picture-based approach to recommender systems. Inf. Technol. Tourism 15(1), 49–69 (2015)CrossRefGoogle Scholar
  16. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer (2011)Google Scholar
  17. Schmunk, S., Höpken, W., Fuchs, M., Lexhagen, M.: Sentiment analysis: extracting decision-relevant knowledge from UGC. Inf. Commun. Technol. Tourism 14(1), 253–265 (2014)Google Scholar
  18. Weiss, S.M., Indurkhya, N., Zhang, T.: Fundamentals of Predictive Text Mining. Springer, London (2010)CrossRefGoogle Scholar
  19. Werthner, H., Klein, S.: Information Technology and Tourism—A Challenging Relationship. Springer, Wien, New York (1999)CrossRefGoogle Scholar
  20. Xiang, Z., Du, Q., Ma, Y., Fan, W.: Assessing reliability of social media data: lessons from mining TripAdvisor hotel reviews. Inf. Commun. Technol. Tourism 17(1), 625–637 (2017)Google Scholar
  21. Yiannakis, A., Gibson, H.: Roles tourists play. Ann. Tourism Res. 19(2), 287–303 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Lisa Glatzer
    • 1
    Email author
  • Julia Neidhardt
    • 1
  • Hannes Werthner
    • 1
  1. 1.E-Commerce GroupTU WienViennaAustria

Personalised recommendations