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 JournalArticleAcademicpeer-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 languageEnglish
Pages (from-to)430-443
Number of pages14
JournalPervasive and Mobile Computing
Volume38
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

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

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