User Community Partition Based on Multi-layer Information Fusion in E-commerce Heterogeneous Network

被引：0
|
作者：
Yong F. [1 ]
Wentao X. [1 ]
Rongbing W. [1 ]
Hongyan X. [1 ]
Yonggang Z. [2 ]
机构：
[1] College of Information, Liaoning University, Shenyang
[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 05期
关键词：
Community Division; E-commerce; Heterogeneous Network; Representation Learning;
D O I：
10.11925/infotech.2096-3467.2021.1068
中图分类号：
学科分类号：
摘要：
[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. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：89 / 98
页数：9
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