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User Community Partition Based on Multi-layer Information Fusion in E-commerce Heterogeneous Network |
Feng Yong1,Xu Wentao1,Wang Rongbing1(),Xu Hongyan1,Zhang Yonggang2 |
1College of Information, Liaoning University, Shenyang 110036, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China |
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Abstract [Objective] This paper proposes a new algorithm based on multi-layer information fusion in an e-commerce heterogeneous network, aiming to improve the accuracy of user community division. [Methods] First, we conducted hierarchical processing of the e-commerce heterogeneous networks and constructed user node embeddings based on different relationship types. Then, we merged users of different layers and obtained their embedding characterization in e-commerce heterogeneous networks. Third, we used the objective function to optimize the relevant parameters of the user nodes. Finally, we clustered these users with an improved K-means algorithm, and created the reasonable community division. [Results] The NMI and Sim@5 indicators of the proposed algorithm were 6.4% and 1.7% higher than the existing algorithms based on DeepWalk, Node2Vec, and GCN. The model effectively characterized user nodes and accurately divided their communities. [Limitations] We did not examine the time information and noise points from the heterogeneous network. [Conclusions] The proposed algorithm could improve the performance of friend prediction, group recommendation and other applications.
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Received: 22 September 2021
Published: 21 June 2022
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Fund:Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University(93K172018K01);General Project of Scie.pngic Research Funds of Liaoning Provincial Department of Education(LJKZ0085) |
Corresponding Authors:
Wang Rongbing,ORCID:0000-0003-4129-7093
E-mail: wrb@lnu.edu.cn
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