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A Dynamic Recommender System for Online Judges Based on Autoencoder Neural Networks

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Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. Workshops (MIS4TEL 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1236))

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Abstract

In recent years, we have witnessed the raising popularity of programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC). In order to train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks.

In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In this paper we present a dynamic Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN).

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Notes

  1. 1.

    http://surpriselib.com.

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Authors and Affiliations

  1. Italian Association for Informatics and Automatic Calculus (AICA), Milano, Italy

    Paolo Fantozzi & Luigi Laura

  2. Sapienza University of Rome, Rome, Italy

    Paolo Fantozzi

  3. International Telematic University Uninettuno, Rome, Italy

    Luigi Laura

Authors
  1. Paolo Fantozzi
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  2. Luigi Laura
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Corresponding author

Correspondence to Luigi Laura .

Editor information

Editors and Affiliations

  1. Department of Informatics Education, Faculty of Mathematics, Physics, and Informatics, Comenius University, Bratislava, Slovakia

    Zuzana Kubincová

  2. Department of Life, Health and Environmental Sciences, University of L’Aquila, Coppito, Italy

    Loreto Lancia

  3. Department of Computers and Information Technology, University of Craiova, Craiova, Romania

    Elvira Popescu

  4. Tokyo Institute of Technology, School of Engineering, Tokyo, Japan

    Minoru Nakayama

  5. Dipartimento di Informatica, Università di Salerno, Fisciano, Italy

    Vittorio Scarano

  6. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Ana B. Gil

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Fantozzi, P., Laura, L. (2021). A Dynamic Recommender System for Online Judges Based on Autoencoder Neural Networks. In: Kubincová, Z., Lancia, L., Popescu, E., Nakayama, M., Scarano, V., Gil, A. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. Workshops. MIS4TEL 2020. Advances in Intelligent Systems and Computing, vol 1236. Springer, Cham. https://doi.org/10.1007/978-3-030-52287-2_20

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