GECKOmmender: Personalised Theme and Tour Recommendations for Museums

  • Fabian Bohnert
  • Ingrid Zukerman
  • Junaidy Laures
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7379)


We present Gecko mmender, a mobile system for personalised theme and tour recommendations in museums, based on a digital site-map representation. Star ratings provided by visitors for seen exhibits are used to predict ratings for unvisited exhibits. The predicted ratings in turn form the basis for recommendations. These recommendations are presented in one of three display modes: StarMap– stars on the site map, HeatMap– colours from green to red that indicate the interestingness of exhibits (from interesting to not interesting respectively), and TourPlann – directed personalised tours through the museum. Gecko mmender was evaluated in a field study at Melbourne Museum (Melbourne, Australia). Our results show that (1) most participants enjoyed Gecko mmender, (2) Gecko mmender’s recommendations often reflected the participants’ personal interests, and (3) HeatMap was the most popular display mode.


Travel Salesman Problem Travel Salesman Problem Display Mode Museum Visitor Personalise Theme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bohnert, F., Zukerman, I.: Non-intrusive Personalisation of the Museum Experience. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 197–209. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Bohnert, F., Zukerman, I.: Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 656–665. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Cheverst, K., Mitchell, K., Davies, N.: The role of adaptive hypermedia in a context-aware tourist GUIDE. Communications of the ACM 45(5), 47–51 (2002)CrossRefGoogle Scholar
  4. 4.
    Feillet, D., Dejax, P., Gendreau, M.: Traveling salesman problems with profits. Transportation Science 39(2), 188–205 (2005)CrossRefGoogle Scholar
  5. 5.
    ten Hagen, K., Modsching, M., Kramer, R.: A location aware mobile tourist guide selecting and interpreting sights and services by context matching. In: Proceedings of the 2nd Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous 2005), Washington, DC, pp. 293–304 (2005)Google Scholar
  6. 6.
    Malaka, R., Zipf, A.: Deep Map – Challenging IT research in the framework of a tourist information system. In: Proceedings of the 7th International Conference on Information and Communication Technologies in Tourism (ENTER 2000), Barcelona, Spain, pp. 15–27 (2000)Google Scholar
  7. 7.
    Oppermann, R., Specht, M.: A Context-Sensitive Nomadic Exhibition Guide. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 127–142. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Stock, O., Zancanaro, M., Busetta, P., Callaway, C., Krüger, A., Kruppa, M., Kuflik, T., Not, E., Rocchi, C.: Adaptive, intelligent presentation of information for the museum visitor in PEACH. User Modeling and User-Adapted Interaction 18(3), 257–304 (2007)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Aroyo, L., Stash, N., Sambeek, R., Schuurmans, Y., Schreiber, G., Gorgels, P.: Cultivating personalized museum tours online and on-site. Interdisciplinary Science Reviews 34(2), 141–156 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fabian Bohnert
    • 1
  • Ingrid Zukerman
    • 1
  • Junaidy Laures
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

Personalised recommendations