Bayesian Personalized Ranking based on Knowledge Graph

Abstract

Collaborative filtering algorithms have serious data sparsity and cold start problems as the amount of data increases and the movie dataset keeps growing.To solve the above problems, this paper proposes to combine the knowledge graph with Matrix factorization algorithm.Through the user's historical interests, mining the user's similar interests on the knowledge graph, to form the candidate items, useing eventually to predict users' interests, and finally using Bayesian personalized recommendation to predict the user's rating of the candidate items to achieve top-K recommendation.Through experiments, it is demonstrated that the algorithm proposed in this paper significantly improves the recommendation effect of matrix decomposition model. With its AUC=0.9348 and ACC=0.8474 on the movie dataset, the experimental data show that the algorithm can improve the recommendation effect more effectively.