Content-based recommender systems for heritage: Developing a personalised museum tour

Olga Loboda, Julianne Nyhan, Simon Mahony, Daniela Romano, Melissa Terras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

How will a content-based recommender system be perceived by museum visitors? How will it transform visitor experience, and how can we adapt recommender systems to meet the needs of users in the museum domain? In this paper, we demonstrate the implementation of a content-based recommender system to generate personalised museum tours within the UCL Grant Museum of Zoology, London, UK. We also outline pilot usability tests that were carried out to collect initial feedback on the system performance in the wild. The findings help detect critical issues before the system is tested with museum visitors to explore the potential transformation in visitor experience that occurs with content-based recommender systems in physical museums.
Original languageEnglish
Title of host publicationProceedings of the 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019)
Subtitle of host publicationThe Alan Turing Institute, London, United Kingdom, 21-22nd November 2019
EditorsIván Palomares
Pages11-16
Number of pages6
ISBN (Electronic)9781526208200
Publication statusPublished - 21 Nov 2019
EventProc. 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems - The Alan Turing Institute, London, United Kingdom
Duration: 21 Nov 201922 Nov 2019
https://dsrs-turing.github.io

Conference

ConferenceProc. 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems
Abbreviated title(DSRS-Turing 2019)
CountryUnited Kingdom
CityLondon
Period21/11/1922/11/19
Internet address

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