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User Privacy in Recommender Systems

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13982))

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Abstract

Recommender systems process abundances of user data to generate recommendations that fit well to each individual user. This utilization of user data can pose severe threats to user privacy, e.g., the inadvertent leakage of user data to untrusted parties or other users. Moreover, this data can be used to reveal a user’s identity, or to infer very private information as, e.g., gender. Instead of the plain application of privacy-enhancing techniques, which could lead to decreased accuracy, we tackle the problem itself, i.e., the utilization of user data. With this, we aim to equip recommender systems with means to provide high-quality recommendations that respect users’ privacy.

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Acknowledgements

Thanks to my supervisors Dominik Kowald and Elisabeth Lex for their feedback on this work. This work is supported by the “DDAI” COMET Module within the COMET - Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG.

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Authors and Affiliations

  1. Know-Center GmbH, Graz, Austria

    Peter Müllner

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  1. Peter Müllner
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Correspondence to Peter Müllner .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Müllner, P. (2023). User Privacy in Recommender Systems. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_52

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  • DOI: https://doi.org/10.1007/978-3-031-28241-6_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28240-9

  • Online ISBN: 978-3-031-28241-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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