Recommending art online

Investigating User Engagement and Interactions With a Digital Collection

Bibliographic Data

ID5905381
AuthorsL Hughes-Noehrer (0000-0002-9167-0397, The University of Manchester, corresponding author), J Carlton (0000-0002-2695-0480, The University of Manchester), Cleo Jay (0000-0002-6080-1382, The University of Manchester)
Year2025
Pages1-28
Publication date2025-06-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMuseum Management and Curatorship (JOURNAL)
Journal identifiersISSN: 0964-7775 • E-ISSN: 1872-9185
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/09647775.2025.2512316
OpenAlexW4411035501
LanguageEN
Citations received1
References cited60

Museum online collections now contain millions of objects, making developing tools for supporting users in navigating these a priority. We present a user-centric study of a recommendation system created to browse Art UK’s digital collection according to personal preference. Three forms of recommendations were explored: image-only, metadata-only, and a combination of the two. Recommendations were evaluated against a baseline of artworks chosen at random. To create a holistic picture of user engagement, both subjective ratings and interaction data were collected. Whilst ratings data showed a uniformly positive perception of the system, the interaction data showed significantly deeper and longer engagement with the artworks that were recommended. Results indicate (1) that recommendations can be a useful tool to help users explore online art collections, and (2) that when evaluating recommendation systems, interaction data can capture patterns of increased engagement that are not evident in subjective ratings

Data science · Human–computer interaction · World Wide Web · Aesthetic Perception and Analysis · Computer Science · Museums and Cultural Heritage · Recommender Systems and Techniques

Tools

Open DOIOpen Access