Automatic Persistent Personalization of Ads in Tourism Websites

  • Alberto RezolaEmail author
  • Aitor Gutierrez
  • Maria Teresa Linaza
Conference paper


Information and Communication Technologies (ICT) have dramatically increased the ability of advertisers to target advertising campaigns and make sure that ads are shown to only certain targeted groups of people. Usage of appropriate ads to each visitor may increase Click Through Rates (CTR) and chances of conversion. This paper presents a novel online advertising approach for automatic “persistent personalization” of Web ads on the basis of Web-mining techniques that combine representative parameters for advertising in a unique platform. The functionality of the approach as well as the problems that arose during the implementation are posed and discussed. Finally, the recommendation system has been successfully validated in a travel blog Website. The implemented prototype made it possible to serve the appropriate ads to the targeted audience on the basis of the classification of user profiles. The obtained CTR was the double of the expected common CTR rates in online advertising campaigns.


Ad personalization Persistent personalization Supervised learning Implicit user profiling Hybrid recommender system 



Authors would like to thank the Basque Government for partially funding this project. Authors would also like to thank the staff of Goiena, Basquetour and Grupo Turiskopio for their valuable help and participation on the validation of the project.


  1. Bae, S. M., Park, S. C., & Ha, S. H. (2003). Fuzzy web ad selector based on web usage mining. IEEE Intelligent Systems, 18(6), 62–69.CrossRefGoogle Scholar
  2. Barford, P., Canadi, I., Krushevskaja, D., Ma, Q., & Muthukrishnan, S. (2014). Adscape: Harvesting and analyzing online display ads. IW3C2, 597–608.Google Scholar
  3. Bilenko, M., & Richardson, M. (2011). Predictive client-side profiles for personalized advertising. Proceedings of the 17th ACM SIGKDD – KDD ’11, 413.Google Scholar
  4. Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. MARKET SCI, (0).Google Scholar
  5. Cole, S. (2008). Creative insights on rich media (Tech Rep). DoubleClick Research Google Scholar
  6. Davis, H. (2006). Google advertising tools: Cashing in with adsense. In Adwords, and the Google APIs. O’Reilly Media.Google Scholar
  7. Donnell, K. O., & Cramer, H. (2015). People’s perceptions of personalized ads. In IW3C2 (pp. 1293–1298).Google Scholar
  8. Ganjisaffar, Y. (2012). Crawler4j–Open Source Web Crawler for Java.Google Scholar
  9. Goldfarb, A., & Tucker, C. (2011). Rejoinder – Implications of “online display advertising: Targeting and obtrusiveness”. Marketing Sci, 30(3), 413–415.CrossRefGoogle Scholar
  10. Idemudia, E. C. (2014). The visual-cognitive model for internet advertising in online market places. International Journal of Online Marketing, 4(3), 31–50.CrossRefGoogle Scholar
  11. Kazienko, P., & Adamski, M. (2007). AdROSA-Adaptive personalization of web advertising. Information Sciences, 177(11), 2269–2295.CrossRefGoogle Scholar
  12. Kohlschütter, C., Fankhauser, P., & Nejdl, W. (2010). Boilerplate detection using shallow text features. In WSDM 2010, New York City (pp. 441–450).Google Scholar
  13. Luna-Nevarez, C., & Hyman, M. R. (2012). Common practices in destination website design. Journal of Destination Marketing and Management, 1(1–2), 94–106.CrossRefGoogle Scholar
  14. Morton, T., Kottmann, J., Baldridge, J., & Bierner, G. (2005).Opennlp: A java-based nlp toolkitGoogle Scholar
  15. Rusmevichientong, P., & Williamson, D. P. (2006). An adaptive algorithm for selecting profitable keywords for search-based advertising services. ACM-EC, pp. 260–269.Google Scholar
  16. Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. In Applications of Data Mining to Electronic Commerce (pp. 115–153). Springer US.Google Scholar
  17. Tsang, M. M., Su-Chun, H., & Ting-Peng, L. (2004). Consumer attitudes towards mobile advertising: An empirical study. International Journal of Electronics and Communications, 8(3), 65–78.Google Scholar
  18. Tucker, C. E. (2012). The economics of advertising and privacy. International Journal of Industrial Organization, 30(3), 326–329.CrossRefGoogle Scholar
  19. Wang, C., Zhang, P., Choi, R., & Eredita, M. D. (2002). Understanding consumers attitude toward advertising. AMCIS, 2002, 1143–1148.Google Scholar
  20. Watson, C., McCarthy, J., & Rowley, J. (2013). Consumer attitudes towards mobile marketing in the smart phone era. International Journal of Information Management, 33(5), 840–849.CrossRefGoogle Scholar
  21. Zanot, E. J. (1984). Public attitudes towards advertising. International Journal of Advertising, 3, 3–15.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alberto Rezola
    • 1
    Email author
  • Aitor Gutierrez
    • 1
  • Maria Teresa Linaza
    • 1
  1. 1.eTourism and Cultural Heritage DepartmentVicomtech-IK4Donostia-San SebastianSpain

Personalised recommendations