- David Walsh ORCID:orcid.org/0000-0003-2972-823312,14,
- Paul Clough ORCID:orcid.org/0000-0003-1739-175X14,15,
- Mark Michael Hall ORCID:orcid.org/0000-0003-0081-427713,
- Frank Hopfgartner ORCID:orcid.org/0000-0003-0380-608814 &
- …
- Jonathan Foster ORCID:orcid.org/0000-0002-9439-088414
Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 12866))
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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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Authors and Affiliations
Edge Hill University, Ormskirk, Lancashire, UK
David Walsh
The Open University, Milton Keynes, UK
Mark Michael Hall
University of Sheffield, Sheffield, UK
David Walsh, Paul Clough, Frank Hopfgartner & Jonathan Foster
Peak Indicators, Chesterfield, UK
Paul Clough
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Correspondence toDavid Walsh.
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Editors and Affiliations
OsloMet – Oslo Metropolitan University, Oslo, Norway
Gerd Berget
The Open University, Milton Keynes, UK
Mark Michael Hall
Martin Luther University Halle-Wittenberg, Halle, Germany
Daniel Brenn
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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