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International Symposium on Methodologies for Intelligent Systems

ISMIS 2020: Foundations of Intelligent Systems pp 395–404Cite as

Exploiting Answer Set Programming for Building explainable Recommendations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

Abstract

The capability of a recommendation system to justify its proposals becomes an ever more important aspect in light of recent legislation and skeptic users. Answer Set Programming (ASP) is a logic programming paradigm aiming at expressing complex problems in a succinct and declarative manner. Due to its rich set of high level language constructs it turns out that ASP is also perfectly suitable for realizing knowledge and/or utility-based recommendation applications, since every aspect of such a utility-based recommendation capable of producing explanations can be specified within ASP. In this paper we give an introduction to the concepts of ASP and how they can be applied in the domain of recommender systems. Based on a small excerpt of a real life recommender database we exemplify how utility based recommendation engines can be implemented with just some few lines of code and show how meaningful explanations can be derived out of the box.

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Notes

  1. 1.

    See https://eugdpr.org/ for reference.

  2. 2.

    For an in-depth introduction to ASP please refer to [6].

  3. 3.

    https://www.techradar.com/news/netflix-vs-amazon-prime-video-which-streaming-service-is-best-for-you.

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Author information

Authors and Affiliations

  1. Universität Klagenfurt, Klagenfurt, Austria

    Erich Teppan

  2. Free University of Bolzano, Bolzano, Italy

    Markus Zanker

Authors
  1. Erich Teppan
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  2. Markus Zanker
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Corresponding authors

Correspondence to Erich Teppan or Markus Zanker .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Teppan, E., Zanker, M. (2020). Exploiting Answer Set Programming for Building explainable Recommendations. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_37

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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