基于知识图谱卷积网络的学习资源推荐

doi:10.19678/j.issn.1000-3428.0068409

计算机工程 ›› 2024, Vol. 50 ›› Issue (9): 153-160. doi: 10.19678/j.issn.1000-3428.0068409

基于知识图谱卷积网络的学习资源推荐

汤志康¹, 武毓琦¹, 李春英¹^,²^,*(), 汤庸³

1. 广东技术师范大学计算机科学学院, 广东广州 510665
2. 广东技术师范大学广东省知识产权大数据重点实验室, 广东广州 510665
3. 华南师范大学计算机学院, 广东广州 510631

收稿日期:2023-09-17 出版日期:2024-09-15 发布日期:2024-03-06
通讯作者: 李春英
基金资助:
国家自然科学基金(61807009); 广东省普通高校重点领域专项(2023ZDZX1009); 广东省知识产权大数据重点实验室开放课题; 广东技术师范大学智能教育联合实验室项目(GSZLGC2023004)

Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network

TANG Zhikang¹, WU Yuqi¹, LI Chunying¹^,²^,*(), TANG Yong³

1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, Guangdong, China
2. Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, Guangdong, China
3. School of Computer, South China Normal University, Guangzhou 510631, Guangdong, China

Received:2023-09-17 Online:2024-09-15 Published:2024-03-06
Contact: LI Chunying

摘要/Abstract

摘要：

针对现有知识图谱卷积网络(KGCN)推荐模型随机采样选择邻域容易导致推荐结果不稳定的缺点, 构建基于结构洞和共同邻居的重要性排序采样模型(SHCN), 结合KGCN处理高维异构数据的优势, 提出基于结构洞和共同邻居的KGCN推荐模型(KGCN-SHCN)。首先使用SHCN模型对知识图谱中的实体邻域进行排序采样, 其次根据图卷积网络将实体信息与邻域采样信息进行聚合得到学习资源的特征表示, 最后将学习者的特征表示和学习资源的特征表示依据预测函数得到交互概率。在3个学习资源数据集上的实验结果表明, 所提模型尤其是使用求和聚合(Sum)方式时, 评价指标AUC和ACC总体优于KGCN、RippleNet等基于知识图谱的推荐模型, 证明了所提KGCN-SHCN模型的有效性。

关键词: 知识图谱, 图卷积网络, 图采样, 推荐算法, 学习资源

Abstract:

Aiming at random sampling and the selection of neighborhoods that may lead to unstable recommendation results in existing Knowledge Graph Convolutional Network(KGCN) models, this study constructs a sampling model for Structural Holes and Common Neighbors(SHCN) importance ranking. SHCN leverages the advantages of KGCN in processing higher-dimensional heterogeneous data. This study proposes a KGCN recommendation model based on SHCN, named KGCN-SHCN. First, the SHCN sampling method is used to sort the receiving domain of each entity in a Knowledge Graph(KG). Then, the entity information and information collected from the entity neighborhood are aggregated according to a Graph Convolutional Network(GCN) to obtain the feature representation of the learning resources. Finally, the feature representations of learners and learning resources are obtained using a prediction function to obtain the interaction probabilities. Experiments are conducted on three datasets, and the experimental results show that the proposed model, especially using the sum aggregation, yields better results in terms of the AUC and ACC evaluation indexes than the KGCN, RippleNet, and other recommendation models based on KG. These results prove that the proposed model is superior.

Key words: Knowledge Graph(KG), Graph Convolutional Network(GCN), graph sampling, recommendation algorithm, learning resource

汤志康, 武毓琦, 李春英, 汤庸. 基于知识图谱卷积网络的学习资源推荐[J]. 计算机工程, 2024, 50(9): 153-160.

TANG Zhikang, WU Yuqi, LI Chunying, TANG Yong. Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network[J]. Computer Engineering, 2024, 50(9): 153-160.

https://www.ecice06.com/CN/Y2024/V50/I9/153

图/表 9

图1 结构洞示意图

Fig.1 Schematic diagram of structural holes

图2 KGCN-SHCN整体框架

Fig.2 Whole framework of KGCN-SHCN

图3 不同k值下AUC和ACC的实验结果

Fig.3 The experimental results of AUC and ACC in different k

图4 不同d值下AUC和ACC的实验结果

Fig.4 The experimental results of AUC and ACC in different d

图5 不同S值下AUC和ACC的实验结果

Fig.5 The experimental results of AUC and ACC in different S

图6 消融实验结果

Fig.6 The results of ablation experiment

参考文献 26

1	PALVIA S, AERON P, GUPTA P, et al. Online education: worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 2018, 21(4): 233- 241. doi: 10.1080/1097198X.2018.1542262
2	ABDELRAHMAN G, WANG Q, NUNES B. Knowledge tracing: a survey. ACM Computing Surveys, 2023, 55(11): 1- 37.
3	CHOI Y, LEE Y, SHIN D, et al. EdNet: a large-scale hierarchical dataset in education[C]//Proceedings of the 21st International Conference on Artificial Intelligence in Education. Berlin, Germany: Springer, 2020: 69-73.
4	ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734- 749. doi: 10.1109/TKDE.2005.99
5	BULATHWELA S, PEREZ-ORTIZ M, YILMAZ E, et al. TrueLearn: a family of Bayesian algorithms to match lifelong learners to open educational resources[C]//Proceedings of the AAAI Conference on Artificial Intelligence. New York, USA: AAAI Press, 2020: 565-573.
6	吴正洋, 汤庸, 刘海. 个性化学习推荐研究综述. 计算机科学与探索, 2022, 16(1): 21- 40. URL
	WU Z Y, TANG Y, LIU H. Survey of personalized learning recommendation. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 21- 40. URL
7	WAN S S, NIU Z D. A hybrid e-learning recommendation approach based on learners' influence propagation. IEEE Transactions on Knowledge and Data Engineering, 2019, 32(5): 827- 840.
8	ZHANG S, YAO L N, SUN A X, et al. Deep learning based recommender system. ACM Computing Surveys, 2019, 52(1): 1- 38.
9	WANG H W, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the World Wide Web Conference. New York, USA: ACM Press, 2019: 3307-3313.
10	SHIMIZU R, MATSUTANI M, GOTO M. An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information. Knowledge-Based Systems, 2022, 239, 107970. doi: 10.1016/j.knosys.2021.107970
11	BING Q Y, ZHU Q N, DOU Z C. Cognition-aware knowledge graph reasoning for explainable recommendation[C]//Proceedings of the 16th ACM International Conference on Web Search and Data Mining. New York, USA: ACM Press, 2023: 402-410.
12	WANG F, ZHANG L L, CHEN X C, et al. A personalized self-learning system based on knowledge graph and differential evolution algorithm. Concurrency and Computation: Practice and Experience, 2022, 34(8): e6190. doi: 10.1002/cpe.6190
13	ZHAO Y Y, WANG X, CHEN J W, et al. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems, 2022, 41(2): 1- 26.
14	LI P, LI T C, WANG X, et al. Scholar recommendation based on high-order propagation of knowledge graphs. International Journal on Semantic Web and Information Systems, 2022, 18(1): 1- 19.
15	GUO Q Y, ZHUANG F Z, QIN C, et al. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3549- 3568. doi: 10.1109/TKDE.2020.3028705
16	XU C, HUANG H L, YING X T, et al. HGNN: hierarchical graph neural network for predicting the classification of price-limit-hitting stocks. Information Sciences, 2022, 607, 783- 798. doi: 10.1016/j.ins.2022.06.010
17	MA T, HUANG L T, LU Q Q, et al. KR-GCN: knowledge-aware reasoning with graph convolution network for explainable recommendation. ACM Transactions on Information Systems, 2023, 41(1): 1- 27.
18	GONG J B, WANG S, WANG J L, et al. Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2020: 79-88.
19	SHI J K, YANG K. MPL-TransKR: multi-perspective learning based on transformer knowledge graph enhanced recommendation. IEEE Access, 2023, 11, 40761- 40769. doi: 10.1109/ACCESS.2023.3266835
20	WU B C, KANG Y, ZAN D G, et al. Hierarchical and contrastive representation learning for knowledge-aware recommendation[C]//Proceedings of the IEEE International Conference on Multimedia and Expo. Washington D. C., USA: IEEE Press, 2023: 1050-1055.
21	YANG Y H, HUANG C, XIA L H, et al. Knowledge graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 1434-1443.
22	CHEN J, FAN D Q, QIAN X R, et al. KGCN-LSTM: a graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction. IET Intelligent Transport Systems, 2023, 17(6): 1087- 1103. doi: 10.1049/itr2.12341
23	BOUGHAREB R, SERIDI H, BELDJOUDI S. Explainable recommendation based on weighted knowledge graphs and graph convolutional networks. Journal of Information & Knowledge Management, 2023, 22(3): 2250098.
24	BURT R S. Structural holes[M]. Cambridge, USA: Harvard University Press, 1992: 54-58.
25	WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2018: 417-426.
26	WANG H W, ZHANG F Z, ZHANG M D, et al. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 968-977.

[1]	李泽霖, 吕兆峰, 陈富强, 李克. 基于多跳信息融合的实体对齐模型[J]. 计算机工程, 2024, 50(9): 142-152.
[2]	李华昱, 张智康, 闫阳, 岳阳. 基于知识图谱增强的领域多模态实体识别[J]. 计算机工程, 2024, 50(8): 31-39.
[3]	刘锁兰, 王炎, 王洪元, 朱生升. 基于多流语义图卷积网络的人体行为识别[J]. 计算机工程, 2024, 50(8): 64-74.
[4]	李惠乾, 钟柏昌. 教育知识图谱: 研究进展与未来发展——基于2013—2023年中文核心期刊载文的分析[J]. 计算机工程, 2024, 50(7): 1-12.
[5]	王炼红, 林飞鹏, 李潇瑶, 谌桂枝, 周莉. 融入课程知识图谱的KMAKT预测[J]. 计算机工程, 2024, 50(7): 23-31.
[6]	刘娟, 段友祥, 陆誉翕, 张鲁. 引入知识增强和对比学习的知识图谱补全[J]. 计算机工程, 2024, 50(7): 112-122.
[7]	陈佳玉, 王元龙, 张虎. 基于文本知识增强的问题生成模型[J]. 计算机工程, 2024, 50(6): 86-93.
[8]	江惠珍, 孙艳春, 黄罡. 基于知识图谱的GitHub层次化学习和检索服务[J]. 计算机工程, 2024, 50(5): 16-25.
[9]	代巍, 王丰羽, 冀常鹏. 基于情感增强与双图卷积网络的方面级情感分析[J]. 计算机工程, 2024, 50(5): 120-127.
[10]	王明虎, 石智奎, 苏佳, 张新生. 基于RoBERTa和图增强Transformer的序列推荐方法[J]. 计算机工程, 2024, 50(4): 121-131.
[11]	莫少聪, 陈庆锋, 谢泽, 刘春雨, 邱俊铼. 基于动态图注意力与标签传播的实体对齐[J]. 计算机工程, 2024, 50(4): 150-159.
[12]	张洪程, 李林育, 杨莉, 伞晨峻, 尹春林, 颜冰, 于虹, 张璇. 基于对比学习与语言模型增强嵌入的知识图谱补全[J]. 计算机工程, 2024, 50(4): 168-176.
[13]	夏卫欢, 廖列法, 张守信, 张燕琴. 基于方面-词性感知的方面级情感分析[J]. 计算机工程, 2024, 50(3): 68-77.
[14]	刘多, 张东, 李冠宇. No-CapsE: 一种基于节点共现的四元数胶囊网络知识图谱补全模型[J]. 计算机工程, 2024, 50(10): 80-88.
[15]	杨兴耀, 马帅, 张祖莲, 于炯, 陈嘉颖, 王东晓. 基于偏好感知的去噪图卷积网络社交推荐[J]. 计算机工程, 2024, 50(10): 154-163.

选择文件类型/文献管理软件名称

选择包含的内容

基于知识图谱卷积网络的学习资源推荐

Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于知识图谱卷积网络的学习资源推荐

Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价