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From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems

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Abstract

Session-based recommender systems consider the evolution of user preferences in browsing sessions. Existing studies suggest as next item the one that keeps the user engaged as long as possible. This point of view does not account for the providers’ perspective. In this paper, we highlight side effects over the providers caused by state-of-the-art models. We focus on the music domain and study how artists’ exposure in the recommendation lists is affected by the input data structure, where different session lengths are explored. We consider four session-based systems on three types of datasets, with long, short, and mixed playlist length. We provide measures to characterize disparate treatment between the artists, through a systematic analysis by comparing (i) the exposure received by an artist in the recommendations and (ii) their input representation in the data. Results show that artists for which we can observe a lot of interactions, but offering less items, are mistreated in terms of exposure. Moreover, we show how input data structure may impact the algorithms’ effectiveness, possibly due to preference-shift phenomena

The first two authors contributed equally to this work.

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Acknowledgments

This research was partially funded by project 2017-SGR-341, MISMIS-LANGUAGE (grant No. PGC2018-096212-B-C33) from the Spanish Ministry of Science and Innovation, and NanoMoocs (grant No. COMRDI18-1-0010) from ACCI. L. Boratto and F. Fabbri acknowledge ACCI, for its support under project “Fair and Explainable Artificial Intelligence (FX-AI)”.

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Authors and Affiliations

  1. Universitat de Barcelona, Barcelona, Spain

    Alejandro Ariza & Maria Salamó

  2. Eurecat - Centre Tecnológic de Catalunya, Barcelona, Spain

    Francesco Fabbri & Ludovico Boratto

  3. Pompeu Fabra University, Barcelona, Spain

    Francesco Fabbri

Authors
  1. Alejandro Ariza
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  2. Francesco Fabbri
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  3. Ludovico Boratto
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  4. Maria Salamó
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Corresponding author

Correspondence to Alejandro Ariza .

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, 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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Ariza, A., Fabbri, F., Boratto, L., Salamó, M. (2021). From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems. 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 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

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