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Clustering and Classifying Users from the National Museums Liverpool Website

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Abstract

Museum websites have been designed to provide access for different types of users, such as museum staff, teachers and the general public. Therefore, understanding user needs and demographics is paramount to the provision of user-centred features, services and design. Various approaches exist for studying and grouping users, with a more recent emphasis on data-driven and automated methods. In this paper, we investigate user groups of a large national museum’s website using multivariate analysis and machine learning methods to cluster and categorise users based on an existing user survey. In particular, we apply the methods to the dominant group - general public - and show that sub-groups exist, although they share similarities with clusters for all users. We find that clusters provide better results for categorising users than the self-assigned groups from the survey, potentially helping museums develop new and improved services.

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Author information

Authors and Affiliations

  1. Edge Hill University, Ormskirk, Lancashire, UK

    David Walsh

  2. The Open University, Milton Keynes, UK

    Mark Michael Hall

  3. University of Sheffield, Sheffield, UK

    David Walsh, Paul Clough, Frank Hopfgartner & Jonathan Foster

  4. Peak Indicators, Chesterfield, UK

    Paul Clough

Authors
  1. David Walsh

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  2. Paul Clough

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  3. Mark Michael Hall

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  4. Frank Hopfgartner

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  5. Jonathan Foster

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Corresponding author

Correspondence toDavid Walsh.

Editor information

Editors and Affiliations

  1. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Gerd Berget

  2. The Open University, Milton Keynes, UK

    Mark Michael Hall

  3. Martin Luther University Halle-Wittenberg, Halle, Germany

    Daniel Brenn

  4. Tampere University, Tampere, Finland

    Sanna Kumpulainen

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Walsh, D., Clough, P., Hall, M.M., Hopfgartner, F., Foster, J. (2021). Clustering and Classifying Users from the National Museums Liverpool Website. In: Berget, G., Hall, M.M., Brenn, D., Kumpulainen, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2021. Lecture Notes in Computer Science(), vol 12866. Springer, Cham. https://doi.org/10.1007/978-3-030-86324-1_24

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