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Similar Users or Similar Items? Comparing Similarity-Based Approaches for Recommender Systems in Online Judges

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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

Online judges store hundreds of programming problems but they lack recommendation tools to help users to find relevant problems to solve. In this paper, we extend the exploration of the use of the implicit knowledge derived from the relationships created between users and problems when the users submit their solutions to the online judge. Inspired by collaborative filtering techniques, in this work we compare a user-based and a problem-based approach, both supported by node similarity metrics coming from social network analysis, and we study the inclusion of voting systems in order to rank the problems that best fit for a user in the online judge. Our experiments reveal that the selection of the highest-performing similarity metric is determined by the recommendation method. We also show that the user-based approach outperforms the problem-based approach only when the proposed voting systems are used.

Supported by UCM (Group 910494) and Spanish Committee of Economy and Competitiveness (TIN2014-55006-R).

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Notes

  1. 1.

    https://uva.onlinejudge.org.

  2. 2.

    https://www.aceptaelreto.com (in Spanish).

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Author information

Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, Madrid, Spain

    Marta Caro-Martinez & Guillermo Jimenez-Diaz

Authors
  1. Marta Caro-Martinez
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  2. Guillermo Jimenez-Diaz
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Corresponding author

Correspondence to Guillermo Jimenez-Diaz .

Editor information

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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Caro-Martinez, M., Jimenez-Diaz, G. (2017). Similar Users or Similar Items? Comparing Similarity-Based Approaches for Recommender Systems in Online Judges. 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_7

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  • DOI: https://doi.org/10.1007/978-3-319-61030-6_7

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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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