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.
|Title of host publication||Linking Theory and Practice of Digital Libraries, Proceedings of TPDL 2021|
|Number of pages||10|
|Publication status||Accepted/In press - 9 Jun 2021|
|Name||Lecture Notes in Computer Science (LNCS, LNAI or LNBI)|