RECOMMENDATION ALGORITHM BASED ON COLLABORATIVE KGE-GUIDED ATTENTIVE NETWORK
Recommendation systems are widely used in practical application scenarios,the existing solutions based on knowledge graphs using graph convolutional neural networks ignore effective embedding the rich semantic information contained in the knowledge graph(KG).This paper proposes a collaborative knowledge graph embedding(KGE)-guided attentive network.By incorporating a translational distance model into the knowledge high-order propagation,the proposed algorithm effectively embedded knowledge in triple sets,and a more efficient way of fusion collaborative information and auxiliary knowledge was realized.And by further expanding the end-to-end model CKAN,the design of the recommendation algorithm was completed.The test results in three real recommendation scenarios show that compared with the existing methods such as KGCN,KGNN-LS,and KGAT,the AUC of the proposed algorithm in CTR prediction reaches 0.847 3 on the Last.FM,reaches 0.753 8 on the Book-Crossing,reaches 0.878 3 on the Dianping-Food,and the recall value is better than benchmark algorithms in Top-K recommendation.
Recommender systemCollaborative filteringHeterogeneous information networkKnowledge graph embeddingGraph convolutional neural network
国家科技期刊平台
NSTL
万方数据
维普
