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...

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Vydáno v:Ji suan ji gong cheng Ročník 50; číslo 9; s. 153 - 160
Hlavní autor: TANG Zhikang, WU Yuqi, LI Chunying, TANG Yong
Médium: Journal Article
Jazyk:čínština
angličtina
Vydáno: Editorial Office of Computer Engineering 15.09.2024
Témata:
knowledge graph(kg)|graph convolutional network(gcn)|graph sampling|recommendation algorithm|learning resource
ISSN:1000-3428
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Popis
Shrnutí: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