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An Adaptive Cross-Site User Modelling Platform for Cultural Heritage Websites

Part of the Communications in Computer and Information Science book series (CCIS,volume 733)

Abstract

This paper discusses an adaptive cross-site user modelling platform for cultural heritage websites. The objective is to present the overall design of this platform that allows for information exchange techniques, which can be subsequently used by websites to provide tailored personalisation to users that request it. The information exchange is obtained by implementing a third party user model provider that, through the use of an API, interfaces with custom-built module extensions of websites based on the Web-based Content Management System (WCMS) Drupal. The approach is non-intrusive, not hindering the browsing experience of the user, and has a limited impact on the core aspects of the websites that integrate it. The design of the API ensures user’s privacy by not disclosing personal browsing information to non-authenticated users. The user can enable/disable the cross-site service at any time.

Keywords

  • Cross-site information needs
  • Cross-site user modelling
  • Web information exchange
  • User model provider

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Fig. 3.

Notes

  1. 1.

    Social graphs ‘socially connect’ users with content and products that ‘peers’ like [3].

  2. 2.

    http://www.cultura-strep.eu/.

  3. 3.

    http://tools.ietf.org/html/rfc5849.

  4. 4.

    http://wiki.dbpedia.org/.

  5. 5.

    http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm.

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Acknowledgements

Stefano Marchesin would like to thank Trinity College Dublin for the use of facilities during his stay in 2016. We thank the reviewers for their insights and comments on the earlier version of the manuscript.

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Correspondence to Maristella Agosti .

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Agosti, M., Lawless, S., Marchesin, S., Wade, V. (2017). An Adaptive Cross-Site User Modelling Platform for Cultural Heritage Websites. In: Grana, C., Baraldi, L. (eds) Digital Libraries and Archives. IRCDL 2017. Communications in Computer and Information Science, vol 733. Springer, Cham. https://doi.org/10.1007/978-3-319-68130-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-68130-6_11

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