Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3464-3471.DOI: 10.11772/j.issn.1001-9081.2022111774
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Received:
2022-11-28
Revised:
2023-03-22
Accepted:
2023-03-23
Online:
2023-04-07
Published:
2023-11-10
Contact:
Qiwen SHI
About author:
WANG Yonggui, born in 1967, M. S., professor. His research interests include big data, parallel computing, database, data mining.Supported by:
通讯作者:
时启文
作者简介:
王永贵(1967—),男,内蒙古赤峰人,教授,硕士,CCF会员,主要研究方向:大数据、并行计算、数据库、数据挖掘CLC Number:
Yonggui WANG, Qiwen SHI. Social recommendation by enhanced GNN with heterogeneous relationship[J]. Journal of Computer Applications, 2023, 43(11): 3464-3471.
王永贵, 时启文. 结合异构关系增强图神经网络的社交推荐[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3464-3471.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022111774
数据集 | 用户数 | 项目数 | 用户-项目评分数 | 用户-用户社交数 |
---|---|---|---|---|
Ciao | 7 374 | 105 059 | 282 163 | 111 781 |
Epinions | 26 337 | 139 738 | 664 832 | 487 182 |
Tab. 1 Statistics of datasets
数据集 | 用户数 | 项目数 | 用户-项目评分数 | 用户-用户社交数 |
---|---|---|---|---|
Ciao | 7 374 | 105 059 | 282 163 | 111 781 |
Epinions | 26 337 | 139 738 | 664 832 | 487 182 |
序号 | Ciao | Epinions | ||
---|---|---|---|---|
MAE | RMSE | MAE | RMSE | |
均值 | 0.719 4 | 0.949 5 | 0.777 2 | 1.015 0 |
1 | 0.719 7 | 0.949 8 | 0.771 2 | 1.014 8 |
2 | 0.720 1 | 0.949 5 | 0.782 6 | 1.015 4 |
3 | 0.719 3 | 0.949 3 | 0.771 7 | 1.014 4 |
4 | 0.718 7 | 0.949 1 | 0.775 7 | 1.015 2 |
5 | 0.719 4 | 0.949 7 | 0.785 1 | 1.015 6 |
Tab. 2 Mean results of HR-GNN model running 5 times
序号 | Ciao | Epinions | ||
---|---|---|---|---|
MAE | RMSE | MAE | RMSE | |
均值 | 0.719 4 | 0.949 5 | 0.777 2 | 1.015 0 |
1 | 0.719 7 | 0.949 8 | 0.771 2 | 1.014 8 |
2 | 0.720 1 | 0.949 5 | 0.782 6 | 1.015 4 |
3 | 0.719 3 | 0.949 3 | 0.771 7 | 1.014 4 |
4 | 0.718 7 | 0.949 1 | 0.775 7 | 1.015 2 |
5 | 0.719 4 | 0.949 7 | 0.785 1 | 1.015 6 |
算法 | Ciao | Epinions | ||
---|---|---|---|---|
MAE | RMSE | MAE | RMSE | |
PMF | 0.902 1 | 1.123 8 | 0.995 2 | 1.212 8 |
SocialMF | 0.832 1 | 1.065 7 | 0.883 7 | 1.132 8 |
SoReg | 0.898 7 | 1.094 7 | 0.941 2 | 1.193 6 |
SAMN | 0.811 6 | 1.092 4 | 0.899 5 | 1.189 9 |
EATNN | 0.797 3 | 1.074 2 | 0.866 3 | 1.138 5 |
GraphRec | 0.759 1 | 1.009 3 | 0.844 1 | 1.087 8 |
Danser | 0.740 3 | 0.990 4 | 0.813 9 | 1.075 6 |
ASR | ||||
ConsisRec | 0.739 4 | 0.972 2 | 0.804 6 | 1.049 5 |
HR-GNN | 0.719 4 | 0.949 5 | 0.777 2 | 1.015 0 |
Tab. 3 Experimental results of different models on two datasets
算法 | Ciao | Epinions | ||
---|---|---|---|---|
MAE | RMSE | MAE | RMSE | |
PMF | 0.902 1 | 1.123 8 | 0.995 2 | 1.212 8 |
SocialMF | 0.832 1 | 1.065 7 | 0.883 7 | 1.132 8 |
SoReg | 0.898 7 | 1.094 7 | 0.941 2 | 1.193 6 |
SAMN | 0.811 6 | 1.092 4 | 0.899 5 | 1.189 9 |
EATNN | 0.797 3 | 1.074 2 | 0.866 3 | 1.138 5 |
GraphRec | 0.759 1 | 1.009 3 | 0.844 1 | 1.087 8 |
Danser | 0.740 3 | 0.990 4 | 0.813 9 | 1.075 6 |
ASR | ||||
ConsisRec | 0.739 4 | 0.972 2 | 0.804 6 | 1.049 5 |
HR-GNN | 0.719 4 | 0.949 5 | 0.777 2 | 1.015 0 |
模型 | 数据集 | 平均训练时间/s |
---|---|---|
GraphRec | Ciao | 221.84 |
Epinions | 587.04 | |
Danser | Ciao | 315.46 |
Epinions | 663.12 | |
ASR | Ciao | 269.31 |
Epinions | 729.63 | |
ConsisRec | Ciao | 1 500.55 |
Epinions | 2 818.32 | |
HR-GNN | Ciao | 256.81 |
Epinions | 621.24 |
Tab. 4 Average training time of different models on two datasets
模型 | 数据集 | 平均训练时间/s |
---|---|---|
GraphRec | Ciao | 221.84 |
Epinions | 587.04 | |
Danser | Ciao | 315.46 |
Epinions | 663.12 | |
ASR | Ciao | 269.31 |
Epinions | 729.63 | |
ConsisRec | Ciao | 1 500.55 |
Epinions | 2 818.32 | |
HR-GNN | Ciao | 256.81 |
Epinions | 621.24 |
模型 | 参数量/106 | 模型大小/MB | 占用内存/GB |
---|---|---|---|
GraphRec | 3.88 | 14.81 | 1.49 |
Danser | 3.85 | 14.68 | 1.42 |
ASR | 3.91 | 14.91 | 1.56 |
ConsisRec | 3.82 | 14.56 | 1.38 |
HR-GNN | 3.86 | 14.72 | 1.44 |
Tab. 5 Parametric quantitative analysis of different models
模型 | 参数量/106 | 模型大小/MB | 占用内存/GB |
---|---|---|---|
GraphRec | 3.88 | 14.81 | 1.49 |
Danser | 3.85 | 14.68 | 1.42 |
ASR | 3.91 | 14.91 | 1.56 |
ConsisRec | 3.82 | 14.56 | 1.38 |
HR-GNN | 3.86 | 14.72 | 1.44 |
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