To address the limitations of existing graph neural network-based news recommendation meth-ods,which often suffer from a simplistic modeling of user interests and the inability to rapidly adapt to new node features,a novel global graph-enhanced graph attention network( GGE-GAT) model was proposed in this study.By aggregating neighbor node features using subgraph sampling from a global graph,the pro-posed model comprehensively models user interests by considering both user historical temporal features and category features in a multi-level manner.Extensive experimentation on the MIND dataset demon-strates the superiority of the proposed model over existing baseline network methods.
维普
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