User Perceptions of Relevance and Its Effect on Retrieval in a Smart Textile Archive
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  • 作者:Ben Horsburgh (21) (22)
    Susan Craw (21) (22)
    Dorothy Williams (23)
    Simon Burnett (23)
    Katie Morrison (23)
    Suzanne Martin (24)
  • 关键词:Learning Refined Representation ; Digitisation of Physical Archives ; User Evaluation
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2013
  • 出版时间:2013
  • 年:2013
  • 卷:7969
  • 期:1
  • 页码:164-178
  • 全文大小:824KB
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  • 作者单位:Ben Horsburgh (21) (22)
    Susan Craw (21) (22)
    Dorothy Williams (23)
    Simon Burnett (23)
    Katie Morrison (23)
    Suzanne Martin (24)

    21. School of Computing Science & Digital Media, Robert Gordon University, Aberdeen, Scotland, UK
    22. IDEAS Research Institute, Robert Gordon University, Aberdeen, Scotland, UK
    23. IMaGeS Research Institute, Robert Gordon University, Aberdeen, Scotland, UK
    24. School of Textiles & Design, Heriot-Watt University, Galashiels, Scotland, UK
  • ISSN:1611-3349
文摘
The digitisation of physical textiles archives is an important process for the Scottish textiles industry. This transformation creates an easy access point to a wide breadth of knowledge, which can be used to understand historical context and inspire future creativity. The creation of such archives however presents interesting new challenges, such as how to organise this wealth of information, and make it accessible in meaningful ways. We present a Case Based Reasoning approach to creating a digital archive and adapting the representation of items in this archive. In doing so we are able to learn the important facets describing an item, and therefore improve the quality of recommendations made to users of the system. We evaluate this approach by constructing a user study, which was completed by industry experts and students. We also compare how users interact with both an offline physical case base, and the online digital case base. Evaluation of our representation adaptation, and our comparison of physical and digital archives, highlights key findings that can inform and strengthen the process for creating new case bases.

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