Analyzing Museum Visitors’ Behavior Patterns

  • Massimo Zancanaro
  • Tsvi Kuflik
  • Zvi Boger
  • Dina Goren-Bar
  • Dan Goldwasser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Many studies have investigated personalized information presentation in the context of mobile museum guides. In order to provide such a service, information about museum visitors has to be collected and visitors have to be monitored and modelled in a non-intrusive manner. This can be done by using known museum visiting styles to classify the visiting style of visitors as they start their visit. Past research applied ethnographic observations of the behaviour of visitors and qualitative analysis (mainly site studies and interviews with staff) in several museums to define visiting styles. The current work validates past ethnographic research by applying unsupervised learning approaches to visitors classification. By providing quantitative empirical evidence for a qualitative theory we claim that, from the point of view of assessing the suitability of a qualitative theory in a given scenario, this approach is as valid as a manual annotation of museum visiting styles.


Artificial Neural Network Hide Neuron Manual Annotation Qualitative Theory Museum Visitor 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Massimo Zancanaro
    • 2
  • Tsvi Kuflik
    • 1
  • Zvi Boger
    • 3
    • 4
  • Dina Goren-Bar
    • 1
  • Dan Goldwasser
    • 1
  1. 1.The University of Haifa, Mount Carmel, Haifa, 31905Israel
  2. 2.ITC-irst, via Sommarive 18, 38050 PovoItaly
  3. 3.Ben-Gurion University of the Negev, Beer Sheva,84105Israel
  4. 4.OPTIMAL – Industrial Neural Systems, Be’er Sheva 84243Israel

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