Knowledge Graph and User Interest Based Recommendation Algorithm

Abstract

In order to solve the problems of cold start and data sparsity in the collaborative filtering recommendation algorithm,the knowledge graph with rich semantic information and path information is introduced in this paper.Based on its graph structure,the recommendation algorithm which applies graph neural network to knowledge graph is favored by researchers.The core of the recommendation algorithm is to obtain item features and user features,however,research in this area focuses on better expressing item features and ignoring the representation of user features.Based on the graph neural network,a recommendation algorithm based on knowledge graph and user interest is proposed.The algorithm constructs user interest by introducing an independent user interest capture module,learning user historical information and modeling user interest,so that it is well represented in both users and items.Experimental results show that on the MovieLens dataset,the recommendation algorithm based on knowledge graph and user interest realizes the full use of data,has good results and promotes the accuracy of recommendation.