摘要
【目的】当前用户社区划分算法大多因缺乏对电商网络异构性的考量,导致社区划分准确度不高。为此,本文提出一种电商异构网络中基于多层信息融合的用户社区划分算法。【方法】根据不同关系类型对电商异构网络进行分层处理,构造基于不同关系类型的用户节点嵌入;通过表征融合将不同层的用户嵌入合并,获得电商异构网络中的用户融合嵌入表征;使用目标函数优化用户节点的相关参数;最后,通过改进的K-means算法形成用户聚类,得到合理的用户社区划分结果。【结果】本文所提算法与基于DeepWalk、Node2Vec、GCN等主流用户社区划分算法中的次优算法相比,在NMI和Sim@5指标上分别提升6.4%和1.7%,在有效表征用户节点及精确划分用户社区方面都有良好的表现。【局限】未考虑电商异构网络中所包含的时间信息,同时忽略了网络中噪声点所产生的影响。【结论】本文算法切实有效,在电商领域有助于提升好友预测、群组推荐等核心应用的性能。
[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.
作者
冯勇
徐文韬
王嵘冰
徐红艳
张永刚
Feng Yong;Xu Wentao;Wang Rongbing;Xu Hongyan;Zhang Yonggang(College of Information,Liaoning University,Shenyang 110036,China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第5期89-98,共10页
Data Analysis and Knowledge Discovery
基金
吉林大学教育部符号计算与知识工程重点实验室资助项目(项目编号:93K172018K01)
辽宁省教育厅科学研究基金面上项目(项目编号:LJKZ0085)的研究成果之一。
关键词
异构网络
电子商务
表征学习
社区划分
Heterogeneous Network
E-commerce
Representation Learning
Community Division
