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Potentially Related Commodity Discovery Based on Link Prediction

Author

Listed:
  • Xiaoji Wan

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

  • Fen Chen

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

  • Hailin Li

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China
    Research Center of Applied Statistics and Big Data, Huaqiao University, Xiamen 361021, China)

  • Weibin Lin

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

Abstract

The traditional method of related commodity discovery mainly focuses on direct co-occurrence association of commodities and ignores their indirect connection. Link prediction can estimate the likelihood of links between nodes and predict the existent yet unknown future links. This paper proposes a potentially related commodities discovery method based on link prediction (PRCD) to predict the undiscovered association. The method first builds a network with the discovered binary association rules among items and uses link prediction approaches to assess possible future links in the network. The experimental results show that the accuracy of the proposed method is better than traditional methods. In addition, it outperforms the link prediction based on graph neural network in some datasets.

Suggested Citation

  • Xiaoji Wan & Fen Chen & Hailin Li & Weibin Lin, 2022. "Potentially Related Commodity Discovery Based on Link Prediction," Mathematics, MDPI, vol. 10(19), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3713-:d:938219
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    References listed on IDEAS

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