Skip to main content
SpringerLink
Log in

Machine Learning and Museum Collections: A Data Conundrum

  • Conference paper
  • First Online:
Emerging Technologies and the Digital Transformation of Museums and Heritage Sites (RISE IMET 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1432))

Abstract

Museums contain vast amounts of information and knowledge, providing a vital source of engagement for diverse audiences. As society becomes ever more digital, museums are moving towards making their collections available online to the public. However, just providing a searchable interface to the entirety of the collection could be a barrier to successful engagement. Tremendous craftsmanship is put into creating interesting and informative in-person curations of selected items, and a challenge exists in replicating this online. One solution could be the application of recommender systems, which personalise information to the individual based on their previous interactions and tastes. These systems power many popular online services, but cannot be applied without considerations and decisions being made about the data that is given to the engine. As museum collections vary in their nature and content, particular care should be taken when handling the data – standard methods may not apply. In this paper, we present the challenges of data curation in the context of using machine learning techniques with museum collections, supported by two case studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Berry, D.M.: The post-digital. the new aesthetic and infrastructural aesthetics. In: Bühler, M. (ed.) No Internet, No Art, 2nd edn. Amsterdam: Onomatopee (2018)

    Google Scholar 

  2. ICOM: Museums, museum professionals and COVID-19’, International Council of Museums (2020). https://icom.museum/wp-content/uploads/2020/05/Report-Museums-and-COVID-19.pdf. Accessed 30 June 2020

  3. Gomez-Uribe, C.A., Hunt, N.: The Netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6(4), 1–19 (2016). https://doi.org/10.1145/2843948

    Article  Google Scholar 

  4. Jacobson, K., Murali, V., Newett, E., Whitman, B., Yon, R.: Music personalization at spotify. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Massachusetts USA, p. 373, September 2016,. https://doi.org/10.1145/2959100.2959120

  5. Rastogi, R.: Machine learning @ Amazon. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor MI USA, pp. 1337–1338, June 2018. https://doi.org/10.1145/3209978.3210211

  6. Smith, B., Linden, G.: Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017). https://doi.org/10.1109/MIC.2017.72

    Article  Google Scholar 

  7. Oumaima, S., Soulaimane, K., Omar, B.: Latest trends in recommender systems applied in the medical domain: a systematic review. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security, Marrakech Morocco, pp. 1–12, March 2020. https://doi.org/10.1145/3386723.3387860

  8. The Royal Society, Machine Learning: The Power and Promise of Computers that Learn by Example (2017)

    Google Scholar 

  9. Jo, E.S., Gebru, T.: Lessons from archives: strategies for collecting sociocultural data in machine learning. In: Proceedings of the ACM FAT* 2020 Conference, Barcelona, Spain, pp. 306–316 (2020)

    Google Scholar 

  10. Suresh, H., Guttag, J.V.: A framework for understanding unintended consequences of machine learning, February 2020 [cs, stat]. http://arxiv.org/abs/1901.10002. Accessed 27 Oct 2020

  11. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  12. Benouaret, I., Lenne, D.: Personalizing the museum experience through context-aware recommendations. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 743–748, October 2015. https://doi.org/10.1109/SMC.2015.139

  13. Keller, I., Viennet, E.: Recommender systems for museums: evaluation on a real dataset. In: Proceedings of the Fifth Conference on Advances in Information Mining and Management, Brussels, pp. 65–71 (2015)

    Google Scholar 

  14. Kontiza, K., Loboda, O., Deladiennee, L., Castagnos, S., Naudet, Y.: A museum app to trigger users’ reflection. In: CEUR Wokshop on Mobile Access to Cultural Heritage, p. 8 (2018)

    Google Scholar 

  15. Koukoulis, K., Koukopoulos, D., Tzortzi, K.: Connecting the museum to the city environment from the visitor’s perspective. Appl. Comput. Inform. S2210832719301991 (2019). https://doi.org/10.1016/j.aci.2019.09.001

  16. Loboda, O., Nyhan, J., Mahony, S., Romano, D.M., Terras, M.: Content-based recommender systems for heritage: developing a personalised museum tour. In: Proceedings of the 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019), p. 7 (2019)

    Google Scholar 

  17. Rossi, S., Barile, F., Galdi, C., Russo, L.: Recommendation in museums: paths, sequences, and group satisfaction maximization. Multimed. Tools Appl. 76(24), 26031–26055 (2017). https://doi.org/10.1007/s11042-017-4869-5

    Article  Google Scholar 

  18. Ruotsalo, T., et al.: SMARTMUSEUM: a mobile recommender system for the Web of Data. Journal of Web Semant. 20, 50–67 (2013). https://doi.org/10.1016/j.websem.2013.03.001

    Article  Google Scholar 

  19. Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A multimedia recommender system. ACM Trans. Internet Technol. 13(1), 1–32 (2013). https://doi.org/10.1145/2532640

    Article  Google Scholar 

  20. Semeraro, G., Lops, P., De Gemmis, M., Musto, C., Narducci, F.: A folksonomy-based recommender system for personalized access to digital artworks. J. Comput. Cult. Herit. 5(3), 1–22 (2012). https://doi.org/10.1145/2362402.2362405

    Article  Google Scholar 

  21. Wang, Y., Stash, N., Aroyo, L., Gorgels, P., Rutledge, L., Schreiber, G.: Recommendations based on semantically enriched museum collections. J. Web Semant. 6(4), 283–290 (2008). https://doi.org/10.1016/j.websem.2008.09.002

    Article  Google Scholar 

  22. Colace, F., De Santo, M., Lombardi, M., Santaniello, D.: CHARS: a cultural heritage adaptive recommender system. In: Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities – TESCA 2019, New York, NY, USA, pp. 58–61 (2019). https://doi.org/10.1145/3364544.3364830.

  23. Hashemi, S.H., Kamps, J.: Exploiting behavioral user models for point of interest recommendation in smart museums. New Rev. Hypermedia Multimed. 24(3), 228–261 (2018). https://doi.org/10.1080/13614568.2018.1525436

    Article  Google Scholar 

  24. Su, X., Sperlì, G., Moscato, V., Picariello, A., Esposito, C., Choi, C.: An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Trans. Industr. Inf. 15(7), 4266–4275 (2019). https://doi.org/10.1109/TII.2019.2908056

    Article  Google Scholar 

  25. Pavlidis, G.: Recommender systems, cultural heritage applications, and the way forward. J. Cult. Heritage 35, 183–196 (2019). https://doi.org/10.1016/j.culher.2018.06.003

    Article  Google Scholar 

  26. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 38 (2019)

    Google Scholar 

  27. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  28. Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci Data 3(1), 160018 (2016). https://doi.org/10.1038/sdata.2016.18

  29. Anderson, S.: Some provocations on the digital future of museums. In: Winesmith, K., Anderson, S. (eds.) The Digital Future of Museums: Conversations and Provocations, 1st edn., Routledge, pp. 10–25 (2020)

    Google Scholar 

  30. Boyd, D., Crawford, K.: Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf. Commun. Soc. 15(5), 662–679 (2012). https://doi.org/10.1080/1369118X.2012.678878

  31. Taylor, J.K.: The Art Museum Redefined: Power, Opportunity, and Community Engagement. Palgrave Macmillan, Cham (2020)

    Book  Google Scholar 

  32. Padilla, T.: On a Collections as Data Imperative. UC Santa Barbara (2017). https://escholarship.org/uc/item/9881c8sv

  33. Gebru, T., et al.: Datasheets for Datasets. arXiv:1803.09010 [cs], March 2020. Accessed 17 Nov 2020

  34. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 82–88 (1996)

    Google Scholar 

  35. Skov, M., Ingwersen, P.: Exploring information seeking behaviour in a digital museum context. In: Proceedings of the second international symposium on Information interaction in context - IIiX 2008, London, United Kingdom, p. 110 (2008). https://doi.org/10.1145/1414694.1414719.

  36. Murray, J.H.: Hamlet on the Holodeck. The Future of Narrative in Cyberspace. The Free Press, New York (1997)

    Google Scholar 

  37. Simon, N.: The Participatory Museum. Santa Cruz, CA: Museum 20 (2010)

    Google Scholar 

  38. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs], September 2013. Accessed 28 Nov 2020

  39. Aggarwal, C.C.: Recommender Systems: The Textbook, 1st edn. Springer, New York (2016). https://doi.org/10.1007/978-3-319-29659-3

  40. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adap. Inter. 12, 331–370 (2002)

    Article  Google Scholar 

  41. Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 265–308. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_8

    Chapter  Google Scholar 

  42. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model User Adap Inter 22(4–5), 441–504 (2012). https://doi.org/10.1007/s11257-011-9118-4

    Article  Google Scholar 

  43. Green, H.E., Courtney, A.: Beyond the scanned image: a needs assessment of scholarly users of digital collections. CRL 76(5), 690–707 (2015). https://doi.org/10.5860/crl.76.5.690

    Article  Google Scholar 

  44. Nekola, J.C., Hutchins, B.T., Schofield, A., Najev, B., Perez, K.E.: Caveat consumptor notitia museo: let the museum data user beware. Glob. Ecol. Biogeogr. 00, 1–13 (2019). https://doi.org/10.1111/geb.12995

    Article  Google Scholar 

  45. Mitchell, E.S.: “Believe Me, I Remain...”. The Mary Greg Collection at Manchester City Galleries. Manchester Metropolitan University, Manchester, UK (2018)

    Google Scholar 

  46. Shepherd, R.: Museum documentation: a hidden problem or something to shout about?. RegistrarTrek: The Next Generation (2014). http://world.museumsprojekte.de/?p=4023. Accessed 08 Nov 2020

  47. Ellin, E.: Museums and the computer: an appraisal of new potentials. Comput. Humanit. 4(1), 25–30 (1969)

    Article  Google Scholar 

  48. Grosvenor, B.: How abolishing museum image fees could boost audiences. In: The Art Newspaper (2018). http://www.theartnewspaper.com/blog/how-abolishing-museum-image-fees-could-boost-audiences. Accessed 24 Nov 2020

  49. Huang, P.H.: Managing digital image licensing services in art history museums. J. Arts Manage. Law Soc. 50(4–5), 220–233 (2020)

    Article  Google Scholar 

  50. Padilla, T.G.: Collections as data: implications for enclosure. C&RL News 79(6), 296 (2018). https://doi.org/10.5860/crln.79.6.296

    Article  Google Scholar 

  51. Bandopadhyay, S.: Digital Culture 2019: Museums, Nesta & Arts Council England (2020). https://media.nesta.org.uk/documents/DC2019-Museums-factsheet.pdf. Accessed 13 Nov 2020

  52. Finnis, J., Kennedy, A.: The Digital Transformation Agenda and GLAMs. A Quick Scan Report for Europeana’, Culture24, July 2020

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Manchester, Manchester, UK

    Lukas Noehrer, Jonathan Carlton & Caroline Jay

  2. BBC R&D, Salford, UK

    Jonathan Carlton

Authors
  1. Lukas Noehrer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan Carlton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caroline Jay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lukas Noehrer .

Editor information

Editors and Affiliations

  1. CYENS Centre of Excellence, Nicosia, Cyprus

    Maria Shehade

  2. Cyprus University of Technology, Limassol, Cyprus

    Theopisti Stylianou-Lambert

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Noehrer, L., Carlton, J., Jay, C. (2021). Machine Learning and Museum Collections: A Data Conundrum. In: Shehade, M., Stylianou-Lambert, T. (eds) Emerging Technologies and the Digital Transformation of Museums and Heritage Sites. RISE IMET 2021. Communications in Computer and Information Science, vol 1432. Springer, Cham. https://doi.org/10.1007/978-3-030-83647-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-83647-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83646-7

  • Online ISBN: 978-3-030-83647-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation