Accurate and personalized recommendation method based on knowledge graph

Abstract

This disclosure provides an accurate and personalized recommendation method based on a knowledge graph, which includes following steps: acquiring relevant knowledge of objects from a knowledge base according to historical behaviors of a user, and constructing a knowledge graph; initializing a vector representation of each node and its connection, and determining a receptive field of the node; generating training samples according to the historical behaviors of the user, and initializing a vector representation of all users and objects; acquiring a receptive field of an entity in the knowledge graph corresponding to the object in the training sample, then inputting the receptive field and the training sample to a graph neural network model to obtain predicted values of a possibility of an interaction between the user and the object. According to the disclosure, a sparsity of the historical behavior information of the original user is compensated with the knowledge graph information, and the user and objects are depicted in multi-dimension, so that the personalized recommendation is more accurate.