IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i1p164-d1313320.html
   My bibliography  Save this article

GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding

Author

Listed:
  • Xulin Ma

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jiajia Tan

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Linan Zhu

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xiaoran Yan

    (Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou 310023, China)

  • Xiangjie Kong

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy. This paper proposes a graph sequence-based model called GSRec that combines Graph Convolutional Network (GCN) and Transformer to solve these problems. In the GCN part we designed a reverse-order graph, and in the Transformer part we introduced the user embedding. The reverse-order graph and the user embedding can make the combination of GCN and Transformer more efficient. Experiments on six datasets show that GSRec outperforms the current state-of-the-art (SOTA) models.

Suggested Citation

  • Xulin Ma & Jiajia Tan & Linan Zhu & Xiaoran Yan & Xiangjie Kong, 2024. "GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding," Mathematics, MDPI, vol. 12(1), pages 1-21, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:1:p:164-:d:1313320
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/1/164/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/1/164/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:1:p:164-:d:1313320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.