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Accurately Predicting User Registration in Highly Unbalanced Real-World Datasets from Online News Portals

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

Getting visitors to register is a crucial factor in marketing for online news portals. Current approaches are rule-based by awarding points for specific actions [3]. Finding efficient rules can be challenging and depends on the specific task. Registration is generally rare compared to regular visitors, leading to highly imbalanced data.

We analyze different supervised learning classification algorithms under consideration of the data imbalance. As case study, we use anonymized real-world data from an Austrian newspaper outlet containing the visitor’s session behavior with around 0.1% registrations over all visits.

We identify an ensemble approach combining the Balanced Random Forest Classifier and the RUSBoost Classifier correctly identifying 76% of registrations over five independent data sets.

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Author information

Authors and Affiliations

  1. Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, Wels, Austria

    Eva-Maria Spitzer & Oliver Krauss

  2. Digital Media Department, University of Applied Sciences Upper Austria, Wels, Austria

    Andreas Stöckl

Authors
  1. Eva-Maria Spitzer
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  2. Oliver Krauss
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  3. Andreas Stöckl
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Corresponding author

Correspondence to Oliver Krauss .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Spitzer, EM., Krauss, O., Stöckl, A. (2022). Accurately Predicting User Registration in Highly Unbalanced Real-World Datasets from Online News Portals. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_23

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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