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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (5): 89-98    DOI: 10.11925/infotech.2096-3467.2021.1068
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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.

Key wordsHeterogeneous Network      E-commerce      Representation Learning      Community Division     
Received: 22 September 2021      Published: 21 June 2022
ZTFLH:  TP302  
  G202  
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

Cite this article:

Feng Yong, Xu Wentao, Wang Rongbing, Xu Hongyan, Zhang Yonggang. User Community Partition Based on Multi-layer Information Fusion in E-commerce Heterogeneous Network. Data Analysis and Knowledge Discovery, 2022, 6(5): 89-98.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1068     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I5/89

Different Types of Networks
UCEH Algorithm Framework
Representation Fusion
数据集 节点类型 节点数量 边类型 边类型对应的网络层 边数量
亚马逊 用户 6 506 用户查看产品 ULP 84 853
电子产品 3 660 用户购买产品 UBP 64 012
Netflix 用户
电影
398 556
81 633
用户评价电影 UEM 17 770
阿里巴巴 用户
商品
4 129
2 034
点击 UKI 4 108
添加至首选项 UPI 3 853
添加到购物车 UCI 7 153
不同类型节点之间转换 CON 2 751
Yelp 用户
商家
34 908
20 502
签到 URS 149 439
评分 UGS 41 843
标记 UMS 38 625
Dataset Comparison
数据集 Amazon Netflix Alibaba Yelp
指标 NMI Sim@5 NMI Sim@5 NMI Sim@5 NMI Sim@5
DeepWalk
Node2Vec
0.083
0.074
0.726
0.738
0.117
0.123
0.490
0.487
0.348
0.382
0.629
0.628
0.311
0.309
0.704
0.710
MetaPath2Vec
DGI
0.086
0.007
0.747
0.558
0.129
0.182
0.492
0.578
0.387
0.551
0.635
0.786
0.317
0.641
0.715
0.889
GCN 0.287 0.624 0.176 0.565 0.465 0.724 0.671 0.867
GAT
HAN
0.301
0.029
0.630
0.495
0.183
0.164
0.550
0.561
0.468
0.472
0.726
0.779
0.668
0.658
0.873
0.872
UCEH-AP 0.341 0.745 0.189 0.603 0.558 0.776 0.685 0.876
UCEH 0.344 0.753 0.194 0.605 0.563 0.787 0.691 0.898
Results of Different Algorithms in Two Types of Experiments
数据集 Amazon Netflix
网络层 ULP UBP UEM
指标 NMI Sim@5 NMI Sim@5 NMI Sim@5
E. 0.002 0.395 0.003 0.414 0.145 0.549
E.+R. 0.002 0.399 0.003 0.426 0.150 0.552
E.+I. 0.152 0.512 0.143 0.517 0.193 0.595
E.+I.+J. 0.169 0.544 0.153 0.525 0.194 0.592
数据集 Alibaba
网络层 UKI UPI UCI CON
指标 NMI Sim@5 NMI Sim@5 NMI Sim@5 NMI Sim@5
E. 0.526 0.698 0.651 0.872 0.089 0.495 0.547 0.801
E.+R. 0.525 0.728 0.659 0.874 0.079 0.490 0.564 0.804
E.+I. 0.527 0.708 0.656 0.882 0.143 0.526 0.569 0.802
E.+I.+J. 0.528 0.716 0.662 0.886 0.142 0.527 0.562 0.805
数据集 Yelp
网络层 URS UGS UMS
指标 NMI Sim@5 NMI Sim@5 NMI Sim@5
E. 0.404 0.740 0.054 0.583 0.038 0.701
E.+R. 0.421 0.741 0.051 0.568 0.020 0.661
E.+I. 0.405 0.741 0.053 0.569 0.401 0.824
E.+I.+J. 0.408 0.742 0.055 0.591 0.407 0.826
The Ablation Experiment of the Algorithm in the Two Types of Experiments
NMI Value and Attention Weight of Each Layer Network
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