Graph Neural Network Recommendation Model Integrating User Preferences

Abstract

Aiming at the problem that knowledge graph-driven graph neural network recommendation algorithm cannot learn the user and item representations at the same time,a graph neural network recommendation model that integrates user preferences is proposed.The model learns user and item representations from user’s perspective and entity’s perspective respectively.Firstly,the user’s perspective spreads user preferences in the knowledge graph based on user historical interaction records and enhances user representation.Secondly,the entity perspective gathers neighbor information of candidate entities through graph convolu-tional network to enrich the representation of the entity.At the same time,a hybrid layer is designed to capture high-level connectivity and hybrid hierarchical information from both the width and depth aspects to enhance the item representation.The enhanced user representation vector and item representation vector are input to the prediction function to predict the interaction probability.Finally,the fixed-size sampling method and phased training strategy are used to optimize the model.The click-through rate prediction experiment is conducted on the MovieLens-1M data set,and the results show that,compared with the benchmark methods RippleNet and KGCN,its AUC increases by 1.7% and 2.3% respectively.

Keywords

• recommendation system|knowledge graph|graph neural network|preference propagation|personalized recommendation