Recommendation Algorithm for Mining Potential Preference Graph Based on Graph Neural Network
Graph recognition plays an increasingly important role in recommendation systems,and the latest technological trend is to develop end-to-end recommendation models based on graph neural networks.However,existing GNN based models often fail to fully explore the infor-mation in the knowledge graph,simply connecting users to entities in the knowledge graph through projects,without clearly modeling the rela-tionships between users and entities.To this end,a recommendation algorithm UEKR based on graph neural networks is proposed for mining latent preference maps.It dynamically extracts entities of interest to users from collaborative knowledge graphs,models the relationship be-tween users and entities,and constructs a user entity relationship graph to enrich user representation and enhance recommendation perfor-mance.The experimental results on three benchmark datasets showed that UEKR improved AUC indicators by 0.75%to 3.65%and F1 indica-tors by 0.70%to 1.75%compared to the control model.
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