Potentially Related Commodity Discovery Based on Link Prediction

Xiaoji Wan, Fen Chen, Hailin Li () and Weibin Lin
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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
Weibin Lin: College of Business Administration, Huaqiao University, Quanzhou 362021, China

Mathematics, 2022, vol. 10, issue 19, 1-27

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.

Keywords: potentially related commodities; association rule; link prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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