Abstract: How to accurately model user preferences based on existing user behavior and auxiliary information is of great important.Among all kinds of auxiliary information,Knowledge Graph (KG) as a new type of auxiliary information,its nodes and edges contain rich structural information and semantic information,and attracts a growing researchers' attention in recent years.Plenty of studies show that the introduction of KG in personalized recommendation can effectively improve the performance of recommendation,and enhance the rationality and interpretability of recommendation.However,the existing methods either explore the independent meta-paths for user-item pairs over KG,or adopt graph representation learning on whole KG to obtain representations for users and items separately.Although both have achieved certain effects,the former fails to fully capture the structural information of user-item pairs in KG,while the latter ignores the mutual effect between target user and item during the embedding propagation.In order to make up for the shortcomings of the above methods,this paper proposes a new model named User-end and Item-end Knowledge Graph (UIKG),which can effectively capture the correlation between users' personalizedpreferences and items by mining the associated attribute information in their respective KG.Specifically,we learn the user representation vectors from the user KG,and then introduce the user representation vectors into the item KG based on the method of graph convolution neural network to jointly learn the item representation vectors,so as to realize the seamless unity of the user KG and the item KG.Finally,we predict the user preference probability of the item through MLP.Experimental results on open datasets show that,compared with the baseline method,UIKG improves by 2.5%~13.6% on Recall@K index,and 0.4%~5.8% on AUC and F1 indexes.

Key words: combination learning, Graph convolutional neural network, Knowledge graph, Personalized recommendation