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

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。

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.