A Museum Visitors Classification Based On Behavioral and Demographic Features

Mokatren, Moayad; Bogina, Veronika; Wecker, Alan J.; Kuflik, Tsvi

doi:10.1145/3314183.3323864

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Recent research has continued to focus on issues such as user modelling and recommender systems (Almeshari et al, 2019;Castagnos et al, 2019;Dahroug et al, 2019;De Angelis et al, 2017;Deladiennee & Naudet, 2017;Fishwick, 2016;Katifori et al, 2019;Mauro, 2019;Mokatren et al, 2019;Sansonetti et al, 2019).…”

Section: Interpersonalizationmentioning

confidence: 99%

Interpersonalizing Intimate Museum Experiences

Ryding

Spence

Løvlie

et al. 2021

International Journal of Human–Computer Interaction

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We reflect on two museum visiting experiences that adopted the strategy of interpersonalization in which one visitor creates an experience for another. In the Gift app, visitors create personal mini-tours for specific others. In Never let me go , one visitor controls the experience of another by sending them remote instructions as they follow them around the museum. By reflecting on the design of these experiences and their deployment in museums we show how interpersonalization can deliver engaging social visits in which visitors make their own interpretations. We contrast the approach to previous research in customization and algorithmic personalization. We reveal how these experiences relied on intimacy between pairs of visitors but also between visitors and the museum. We propose that interpersonalization requires museums to step-back to make space for interpretation, but that this then raises the challenge of how to reintroduce the museum's own perspective. Finally, we articulate strategies and challenges for applying this approach.

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Section: Interpersonalizationmentioning

confidence: 99%

Interpersonalizing Intimate Museum Experiences

Ryding

Spence

Løvlie

et al. 2021

International Journal of Human–Computer Interaction

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“…Besides, psychological and sociological variables can be observed on side of paths, such as heart rate, skin conductance, emotional and aesthetic evaluations of specific artworks [29,47,48], interactions with groupmates, degree of attention, boredom or fatigue. Automatic systems could be complemented with manual activities, like paper-and-pencil annotations and questionnaires [28,34,47]. From questionnaires one can estimate demographic related and museum visit related features [34] like age, gender, educational level, number of visits per year to museums, etc.…”

mentioning

confidence: 99%

“…Automatic systems could be complemented with manual activities, like paper-and-pencil annotations and questionnaires [28,34,47]. From questionnaires one can estimate demographic related and museum visit related features [34] like age, gender, educational level, number of visits per year to museums, etc. After the visit, one can measure the degree of satisfaction, the relationship between perceived and real time spent in the museum [4], etc.…”

mentioning

confidence: 99%

“…Clustering techniques (e.g., k-means, hierarchical clustering, sequence alignment) have been used to assign every visitor trajectory (spanning from room-scale to continuum) to one of the groups described above, or to some given typical movement patterns [8,15,17,27,34,36,38,41,44,55,57]. This enables one to quantify the percentage of visitors belonging to each group.…”

mentioning

confidence: 99%

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Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization

Centorrino

Corbetta

Cristiani

et al. 2021

Journal of Computational Science

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“…(Levasseur & Veron, 1983;Serrell, 1997;Sookhanaphibarn & Thawonmas, 2009). In contrast, other studies have combined both behavioral signals and demographics (Mokatren et al, 2019). These visitor types have been instrumental in designing exhibitions and the content and activities for exhibitions.…”

Section: Visitor Experiencementioning

confidence: 99%

Exploration Systems: Using Experience Technologies in Automated Exhibition Sites

Krishnasamy¹

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show abstract

A Museum Visitors Classification Based On Behavioral and Demographic Features

Cited by 11 publications

References 8 publications

Interpersonalizing Intimate Museum Experiences

Interpersonalizing Intimate Museum Experiences

Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization

Exploration Systems: Using Experience Technologies in Automated Exhibition Sites

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