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Disparate Impact in Item Recommendation: A Case of Geographic Imbalance

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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

Recommender systems are key tools to push items’ consumption. Imbalances in the data distribution can affect the exposure given to providers, thus affecting their experience in online platforms. To study this phenomenon, we enrich two datasets and characterize data imbalance w.r.t. the country of production of an item (geographic imbalance). We focus on movie and book recommendation, and divide items into two classes based on their country of production, in a majority-versus-rest setting. To assess if recommender systems generate a disparate impact and (dis)advantage a group, we introduce metrics to characterize the visibility and exposure a group receives in the recommendations. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. Results show the presence of a disparate impact that mostly favors the majority; however, factorization approaches are still capable of capturing the preferences for the minority items, thus creating a positive impact for the group. To mitigate disparities, we propose an approach to reach the target visibility and exposure for the disadvantaged group, with a negligible loss in effectiveness.

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Notes

  1. 1.

    https://publications.parliament.uk/pa/cm200203/cmselect/cmcumeds/667/667.pdf.

  2. 2.

    http://www.omdbapi.com/.

  3. 3.

    https://grp.isbn-international.org/search/piid_cineca_solr.

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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 ACCIÓ. L. Boratto acknowledges Agència per a la Competivitat de l’Empresa, ACCIÓ, for their support under project “Fair and Explainable Artificial Intelligence (FX-AI)”.

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Authors and Affiliations

  1. Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain

    Elizabeth Gómez & Maria Salamó

  2. Data Science and Big Data Analytics, Eurecat - Centre Tecnológic de Catalunya, Barcelona, Spain

    Ludovico Boratto

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  1. Elizabeth Gómez
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  2. Ludovico Boratto
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  3. Maria Salamó
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Correspondence to Ludovico Boratto .

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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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Gómez, E., Boratto, L., Salamó, M. (2021). Disparate Impact in Item Recommendation: A Case of Geographic Imbalance. 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_13

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