Recommending art online: investigating user engagement and interactions with a digital collection

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.