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Knowledge-Based Recommendation Systems: A Survey

Author

Listed:
  • Sarah Bouraga

    (Department of Business Administration, PReCISE Research Center, University of Namur, Namur, Belgium)

  • Ivan Jureta

    (Department of Business Administration, PReCISE Research Center, University of Namur, Namur, Belgium)

  • Stéphane Faulkner

    (Department of Business Administration, PReCISE Research Center, University of Namur, Namur, Belgium)

  • Caroline Herssens

    (Department of Business Administration, PReCISE Research Center, University of Namur, Namur, Belgium)

Abstract

Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.

Suggested Citation

  • Sarah Bouraga & Ivan Jureta & Stéphane Faulkner & Caroline Herssens, 2014. "Knowledge-Based Recommendation Systems: A Survey," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 10(2), pages 1-19, April.
    • Handle: RePEc:igg:jiit00:v:10:y:2014:i:2:p:1-19
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijiit.2014040101
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    Cited by:

    1. Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    2. George Stalidis & Iphigenia Karaveli & Konstantinos Diamantaras & Marina Delianidi & Konstantinos Christantonis & Dimitrios Tektonidis & Alkiviadis Katsalis & Michail Salampasis, 2023. "Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing," Sustainability, MDPI, vol. 15(23), pages 1-33, November.

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