Collaborative Knowledge Graph and Graph Convolution Network Based Recommendation Algorithm
The recommendation system is widely used in the Internet to alleviate the problem of information overload.The existing research usually introduces knowledge graph into recommendation algorithm,but it cannot effectively obtain the high-level modeling of users and projects and has the problem of data sparsity.We propose a collaborative knowledge graph and graph convolution network based recommendation algorithm(CKGCN).Firstly,the user project interaction matrix and the project knowledge graph are constructed as a collaborative knowledge graph.The weight of neighbor nodes is allocated using the knowledge awareness attention mechanism,the feature vectors of users and projects are captured recursively,and the potential preferences of users for projects are searched to effectively alleviate the problem of data sparsity.Secondly,the neighborhood aggregation algorithm based on graph convolution network is used to capture the higher-order relationship between each layer of entity network,aggregate entities and neighborhood entities,and enrich entity semantic representation.In addition,the cross-compression unit cooperatively processes the project feature vector and entity feature vector to explore their higher-order feature interaction,so as to filter the redundant information of entities and mine the deeper relationship of projects.Finally,the user feature vector and the project feature vector are calculated to obtain the prediction probability of the user to the project.According to the hit rate prediction and Top-k recommendation experiment,on the two public datasets of Crossing and Music Last.FM,this model is compared with five baseline models,namely,AUC,ACC,F1 Recall@ k and Precision@ k,and the e-valuation index values have been improved,indicating that the model has good recommendation performance.
国家科技期刊平台
NSTL
万方数据
维普
