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Metric Learning for Session-Based Recommendations

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Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12656))

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Abstract

Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.

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Notes

  1. 1.

    https://github.com/btwardow/dml4rec.

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Acknowledgments and Disclosure of Funding

We acknowledge the support from Sales Intelligence and co-funding by European Regional Development Fund, project number: POIR.01.01.01-00-0632/18.

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Bartłomiej Twardowski & Paweł Zawistowski

  2. Computer Vision Center, Universitat Autónoma de Barcelona, Bellaterra, Spain

    Bartłomiej Twardowski

  3. Sales Intelligence, Wrocław, Poland

    Szymon Zaborowski

Authors
  1. Bartłomiej Twardowski
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  2. Paweł Zawistowski
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  3. Szymon Zaborowski
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Corresponding author

Correspondence to Paweł Zawistowski .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Twardowski, B., Zawistowski, P., Zaborowski, S. (2021). Metric Learning for Session-Based Recommendations. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_43

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  • DOI: https://doi.org/10.1007/978-3-030-72113-8_43

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