Analyzing museum visitors' Behavior patterns

被引:0
|
作者
Zancanaro, Massimo [2 ]
Kuflikt, Tsvi [1 ]
Boger, Zvi [3 ,4 ]
GorenBar, Dina [1 ]
Goldwasser, Dan [1 ]
机构
[1] Univ Haifa, IL-31905 Haifa, Israel
[2] ITC Irst, I-38050 Povo, Italy
[3] Univ Negev, Ben Gur, IL-84105 Beer Sheva, Israel
[4] Ind Neural Syst, OPTIMAL, IL-84243 Sheva, Israel
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页码:238 / +
页数:3
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