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Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.

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Notes

  1. 1.

    Code and dataset partitions are available at https://github.com/alarca94/magrec.

  2. 2.

    https://github.com/RuihongQiu/FGNN.

  3. 3.

    https://github.com/RManLuo/MAMDR.

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Acknowledgements

This work was partially supported by the FairTransNLP-Language Project (MCIN/AEI/10.13039/501100011033/FEDER,UE).

Author information

Authors and Affiliations

  1. Universitat de Barcelona, Barcelona, Spain

    Alejandro Ariza-Casabona

  2. Computer Vision Center, UAB, Barcelona, Spain

    Bartlomiej Twardowski

  3. Huawei Ireland Research Center, Dublin, Ireland

    Bartlomiej Twardowski & Tri Kurniawan Wijaya

Authors
  1. Alejandro Ariza-Casabona
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  2. Bartlomiej Twardowski
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  3. Tri Kurniawan Wijaya
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Corresponding author

Correspondence to Alejandro Ariza-Casabona .

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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Ariza-Casabona, A., Twardowski, B., Wijaya, T.K. (2023). Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-28244-7_4

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