Visualizing, clustering, and predicting the behavior of museum visitors

Claudio Martella; Armando Miraglia; Jeana Frost; Marco Cattani; Maarten van Steen

doi:10.1016/j.pmcj.2016.08.011

Visualizing, clustering, and predicting the behavior of museum visitors

Claudio Martella^*, Armando Miraglia, Jeana Frost, Marco Cattani, Maarten van Steen

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.

Original language	English
Pages (from-to)	430-443
Number of pages	14
Journal	Pervasive and Mobile Computing
Volume	38
DOIs	https://doi.org/10.1016/j.pmcj.2016.08.011
Publication status	Published - 1 Jul 2017

Funding

This publication was supported by the Dutch national program COMMIT and by The Network Institute . We would like to thank the staff of CoBrA for all their time and support during this project.

Funders	Funder number
Dutch national program COMMIT
Network Institute

Keywords

Hierarchical clustering
Matrix factorization
Mobile sensors
Museum visitor analysis
Prediction
Proximity sensing
Recommendation
Visualization

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title = "Visualizing, clustering, and predicting the behavior of museum visitors",

abstract = "Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.",

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AU - Martella, Claudio

AU - Miraglia, Armando

AU - Frost, Jeana

AU - Cattani, Marco

AU - van Steen, Maarten

PY - 2017/7/1

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KW - Matrix factorization

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KW - Prediction

KW - Proximity sensing

KW - Recommendation

KW - Visualization

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Visualizing, clustering, and predicting the behavior of museum visitors

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Keywords

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