Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network

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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Ji suan ji gong cheng Jg. 50; H. 9; S. 153 - 160
1. Verfasser:	TANG Zhikang, WU Yuqi, LI Chunying, TANG Yong
Format:	Journal Article
Sprache:	Chinesisch Englisch
Veröffentlicht:	Editorial Office of Computer Engineering 15.09.2024
Schlagworte:	knowledge graph(kg)\|graph convolutional network(gcn)\|graph sampling\|recommendation algorithm\|learning resource
ISSN:	1000-3428
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	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.
ISSN:	1000-3428
DOI:	10.19678/j.issn.1000-3428.0068409