Research of KGCN Recommendation Algorithm Optimization Based on Knowledge Graph
KGCN uses graph convolution to mine the entity neighborhood features in the knowledge graph,but the uniform sampling method used in constructing the entity neighborhood ignores the differences of the properties of each neighbor node.To solve this problem,this paper proposes a non-uniform sampling method of important neighbor sampling,according to the centrality of node degree,the optimization and elimination of neighbor nodes in graph network entity neighborhood feature mining are real-ized,so as to improve the selection quality of sample nodes.In addition,before the input layer of the graph convolution network,a denoising auto encoder is added to reduce the noise interference caused by clicking on the sample by mistake,so as to increase the stability of the training process and prevent over fitting.Finally,this paper presents an improved recommendation algorithm model,which uses the hierarchical propagation mechanism of neural network to iteratively aggregate the node information in a wider range in the graph network and optimize the recommendation effect.This paper makes comparative experiments on the improved recom-mendation algorithm on two data sets of movies and books,the experimental results show that the improved algorithm improves the accuracy and robustness of recommendation.
KGCNknowledge graphdegree centralitydenoising auto encoder
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