Knowledge graph driven recommendation model of graph neural network

A new graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information, was developed to enhance the accuracy and effectiveness of personalized recommendations. This model begins by using a single-layer neural network to merge individual node features in the graph. It then adjusts the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model's performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests using the MovieLen-1M and Book-Crossing datasets, KGLN showed an AUC (Area Under the ROC curve) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, over established benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet.