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A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13944))

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Abstract

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed. Traditional CDR leverages the relatively richer information from a richer domain to improve recommendation performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users between domains, where they extract the embeddings of common users and then transfer the embeddings to the two target domains to improve recommendation performance. However, in real life, the proportion of common users between domains is usually very small, which makes it hard to generate representative and high-quality user embeddings, and thus, limits the performance of the existing methods in real applications. To address this problem, in this paper, we propose a Three-Layer Attentional Framework based on Similar Users, called TASU. In addition to common users, TASU leverages information from similar users to improve the quality of user embeddings. By a three-layer attentional framework, TASU can generate more representative and high-quality user embeddings to improve recommendation performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that TASU significantly outperforms the state-of-the-art approaches.

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Notes

  1. 1.

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Acknowledgements

This work was supported by Shanghai Science and Technology Commission (No. 22YF1401100), Fundamental Research Funds for the Central Universities (No. 22D111210, 22D111207), and National Science Fund for Young Scholars (No. 62202095).

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Authors and Affiliations

  1. Donghua University, Shanghai, China

    Jinhu Lu, Guohao Sun, Xiu Fang & Wei He

  2. School of Computing, Macquarie University, Macquarie Park, Australia

    Jian Yang

Authors
  1. Jinhu Lu
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  2. Guohao Sun
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  3. Xiu Fang
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  4. Jian Yang
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  5. Wei He
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Corresponding author

Correspondence to Guohao Sun .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Lu, J., Sun, G., Fang, X., Yang, J., He, W. (2023). A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-30672-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30671-6

  • Online ISBN: 978-3-031-30672-3

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