Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3252-3259.DOI: 10.11772/j.issn.1001-9081.2023101508
• The 40th CCF National Database Conference (NDBC 2023) • Previous Articles Next Articles
Beijing ZHOU1, Hairong WANG1,2(), Yimeng WANG1, Lisi ZHANG1, He MA1
Received:
2023-11-06
Revised:
2023-12-23
Accepted:
2023-12-28
Online:
2024-10-15
Published:
2024-10-10
Contact:
Hairong WANG
About author:
ZHOU Beijing, born in 1997, M. S. candidate. His research interests include recommendation system based on knowledge graph.Supported by:
周北京1, 王海荣1,2(), 王怡梦1, 张丽丝1, 马赫1
通讯作者:
王海荣
作者简介:
周北京(1997—),男,湖南衡阳人,硕士研究生,CCF会员,主要研究方向:基于知识图谱的推荐系统基金资助:
CLC Number:
Beijing ZHOU, Hairong WANG, Yimeng WANG, Lisi ZHANG, He MA. Recommendation method using knowledge graph embedding propagation[J]. Journal of Computer Applications, 2024, 44(10): 3252-3259.
周北京, 王海荣, 王怡梦, 张丽丝, 马赫. 图谱嵌入传播的推荐方法[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3252-3259.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023101508
模型 | MovieLens-1M | Book-Crossing | ||
---|---|---|---|---|
AUC | ACC | AUC | ACC | |
RippleNet | 0.843 5 | 0.767 1 | 0.771 4 | 0.725 4 |
AKUPM | 0.918 1 | 0.845 2 | 0.843 5 | 0.807 2 |
CIEPA | 0.925 4 | 0.849 6 | 0.853 1 | 0.815 4 |
Tab. 1 Comparative analysis of typical user embedding propagation methods
模型 | MovieLens-1M | Book-Crossing | ||
---|---|---|---|---|
AUC | ACC | AUC | ACC | |
RippleNet | 0.843 5 | 0.767 1 | 0.771 4 | 0.725 4 |
AKUPM | 0.918 1 | 0.845 2 | 0.843 5 | 0.807 2 |
CIEPA | 0.925 4 | 0.849 6 | 0.853 1 | 0.815 4 |
模型 | MovieLens-1M | MovieLens-20M | Book-Crossing | Last.FM | ||||
---|---|---|---|---|---|---|---|---|
AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
KGCN | 0.900 1 | 0.823 0 | 0.976 7 | 0.929 0 | 0.692 2 | 0.635 4 | 0.803 1 | 0.732 0 |
KGNN-LS | 0.902 7 | 0.826 0 | 0.978 5 | 0.934 1 | 0.689 0 | 0.632 3 | 0.804 2 | 0.714 7 |
KGPL | 0.914 7 | 0.835 3 | 0.976 3 | 0.926 7 | 0.757 3 | 0.670 3 | 0.861 2 | 0.767 8 |
CG-KGR | 0.896 9 | 0.815 1 | 0.983 1 | 0.940 3 | 0.759 5 | 0.701 0 | 0.833 3 | 0.758 4 |
Tab. 2 Comparative analysis of typical item embedding propagation methods
模型 | MovieLens-1M | MovieLens-20M | Book-Crossing | Last.FM | ||||
---|---|---|---|---|---|---|---|---|
AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
KGCN | 0.900 1 | 0.823 0 | 0.976 7 | 0.929 0 | 0.692 2 | 0.635 4 | 0.803 1 | 0.732 0 |
KGNN-LS | 0.902 7 | 0.826 0 | 0.978 5 | 0.934 1 | 0.689 0 | 0.632 3 | 0.804 2 | 0.714 7 |
KGPL | 0.914 7 | 0.835 3 | 0.976 3 | 0.926 7 | 0.757 3 | 0.670 3 | 0.861 2 | 0.767 8 |
CG-KGR | 0.896 9 | 0.815 1 | 0.983 1 | 0.940 3 | 0.759 5 | 0.701 0 | 0.833 3 | 0.758 4 |
模型 | MovieLens-1M | MovieLens-20M | Book-Crossing | Last.FM | ||||
---|---|---|---|---|---|---|---|---|
AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
KGAT | 0.910 2 | 0.838 9 | 0.981 3 | 0.912 1 | 0.719 4 | 0.689 9 | 0.805 4 | 0.764 1 |
CKAN | 0.917 8 | 0.844 0 | 0.971 7 | 0.921 8 | 0.745 3 | 0.655 9 | 0.846 3 | 0.746 2 |
KGIN | 0.884 0 | 0.621 3 | 0.989 4 | 0.892 2 | 0.804 2 | 0.738 1 | 0.855 5 | 0.711 0 |
COAT | 0.892 2 | 0.811 2 | 0.978 4 | 0.930 2 | 0.740 7 | 0.697 3 | 0.789 4 | 0.714 1 |
LKGR | 0.917 0 | 0.845 3 | 0.982 9 | 0.941 6 | 0.679 3 | 0.637 2 | 0.796 3 | 0.716 1 |
Tab. 3 Comparative analysis of typical hybrid embedding propagation methods
模型 | MovieLens-1M | MovieLens-20M | Book-Crossing | Last.FM | ||||
---|---|---|---|---|---|---|---|---|
AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
KGAT | 0.910 2 | 0.838 9 | 0.981 3 | 0.912 1 | 0.719 4 | 0.689 9 | 0.805 4 | 0.764 1 |
CKAN | 0.917 8 | 0.844 0 | 0.971 7 | 0.921 8 | 0.745 3 | 0.655 9 | 0.846 3 | 0.746 2 |
KGIN | 0.884 0 | 0.621 3 | 0.989 4 | 0.892 2 | 0.804 2 | 0.738 1 | 0.855 5 | 0.711 0 |
COAT | 0.892 2 | 0.811 2 | 0.978 4 | 0.930 2 | 0.740 7 | 0.697 3 | 0.789 4 | 0.714 1 |
LKGR | 0.917 0 | 0.845 3 | 0.982 9 | 0.941 6 | 0.679 3 | 0.637 2 | 0.796 3 | 0.716 1 |
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