Journal
PERVASIVE AND MOBILE COMPUTING
Volume 38, Issue -, Pages 430-443Publisher
ELSEVIER
DOI: 10.1016/j.pmcj.2016.08.011
Keywords
Proximity sensing; Mobile sensors; Museum visitor analysis; Hierarchical clustering; Visualization; Recommendation; Prediction; Matrix factorization
Funding
- Dutch national program COMMIT
- Network Institute
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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. (C) 2016 Elsevier B.V. All rights reserved.
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