Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Museum Management and Curatorship

DOI: 10.1080/09647775.2025.2512316

Authors: L. Hughes-Noehrer, J. Carlton, C. Jay

Institution: University of Manchester

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Engineering and Physical Sciences Research Council

Awards: Artificial Intelligence and the Useful Art Museum: A Cross-Disciplinary Approach Towards Machine Learning and its Implications in the Museum Sphere, DTP 2018-19 The University of Manchester