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Federated Conversational Recommender Systems

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14612))

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Abstract

Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users in a conversational manner to learn their current and fine-grained preferences, a CRS can quickly derive recommendations that are relevant and justifiable. However, existing CRSs typically rely on a centralized training and deployment process, which involves collecting and storing explicitly-communicated user preferences in a centralized repository. These fine-grained user preferences are completely human-interpretable and can easily be used to infer sensitive information (e.g., financial status, political stands, and health information) about the user, if leaked or breached. To address the user privacy concerns in CRS, we first define a set of privacy protection guidelines for preserving user privacy then propose a novel federated CRS framework that effectively reduces the risk of exposing user privacy. Through extensive experiments, we show that the proposed framework not only satisfies these user privacy protection guidelines, but also achieves competitive recommendation performance comparing to the state-of-the-art non-private conversational recommendation approach.

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Notes

  1. 1.

    A commonly encountered issue in federated reinforcement learning that hinders a uniform policy to deliver optimal interaction experience to every user [20].

  2. 2.

    Note that the choice of predictive model is flexible; we choose FM due to its widely demonstrated success in CRSs.

  3. 3.

    Following [32], we regard each user device that participates in the training of the model as a client.

  4. 4.

    A sample of such user feedback would be:

    System: are you looking for more country music?

    User: Not really, I used to like country music but now I am more into jazz.

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Author information

Authors and Affiliations

  1. Texas A &M University, College Station, TX, USA

    Allen Lin, Jianling Wang & James Caverlee

  2. George Mason University, Fairfax, VA, USA

    Ziwei Zhu

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  1. Allen Lin
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  2. Jianling Wang
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  3. Ziwei Zhu
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  4. James Caverlee
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Corresponding author

Correspondence to Allen Lin .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Lin, A., Wang, J., Zhu, Z., Caverlee, J. (2024). Federated Conversational Recommender Systems. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-56069-9_4

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