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Encouraging Curiosity in Case-Based Reasoning and Recommender Systems

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Case-Based Reasoning Research and Development (ICCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10339))

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Abstract

A key benefit of case-based reasoning (CBR) and recommender systems is the use of past experience to guide the synthesis or selection of the best solution for a specific context or user. Typically, the solution presented to the user is based on a value system that privileges the closest match in a query and the solution that performs best when evaluated according to predefined requirements. In domains in which creativity is desirable or the user is engaged in a learning activity, there is a benefit to moving beyond the expected or “best match” and include results based on computational models of novelty and surprise. In this paper, models of novelty and surprise are integrated with both CBR and Recommender Systems to encourage user curiosity.

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Acknowledgements

These ideas have emerged from years of research in computational creativity and we acknowledge that our ideas have been influenced by the co-authors of our other papers and our PhD students, including Douglas H Fisher, Kate Brady, Katherine Merrick, Dave Wilson, Nadia Najjar, Mohammad Mahzoon, Maryam Mohseni, and Pegah Karimi. The authors acknowledge support from NSF IIS1618810 CompCog: RI: Small: Pique: A cognitive model of curiosity for personalizing sequences of learning resources.

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Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, 28202, USA

    Mary Lou Maher & Kazjon Grace

Authors
  1. Mary Lou Maher
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  2. Kazjon Grace
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Correspondence to Mary Lou Maher .

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Editors and Affiliations

  1. Naval Research Laboratory, Navy Center for Applied Research in AI, Washington, District of Columbia, USA

    David W. Aha

  2. LORIA/Bâtiment B/équipe Orpailleur, University of Lorraine, Vandœuvre-lès-Nancy Cedex, France

    Jean Lieber

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Maher, M.L., Grace, K. (2017). Encouraging Curiosity in Case-Based Reasoning and Recommender Systems. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-61030-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61029-0

  • Online ISBN: 978-3-319-61030-6

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