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Effective and Efficient Transformer Models for Sequential Recommendation

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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

The focus of our work is sequential recommender systems. Sequential recommender systems use ordered sequences of user-item interactions to predict future interactions of the user.

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Author information

Authors and Affiliations

  1. University of Glasgow, Glasgow, UK

    Aleksandr V. Petrov

Authors
  1. Aleksandr V. Petrov
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Corresponding author

Correspondence to Aleksandr V. Petrov .

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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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Petrov, A.V. (2024). Effective and Efficient Transformer Models for Sequential Recommendation. 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_39

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  • DOI: https://doi.org/10.1007/978-3-031-56069-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56068-2

  • Online ISBN: 978-3-031-56069-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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