一种全局图增强的图神经网络新闻推荐算法  

Graph Neural Network News Recommendation Algorithm Based on Global Graph Enhanced

作　　者：杨智勇 陈向东[1] 陈佳慧 YANG Zhi-yong;CHEN Xiang-dong;CHEN Jia-hui(School of Computer and Information Science,Chongqing Normal University,401331,Chongqing;School of Big Data and Internet of Things,Chongqing Vocational Institute of Engineering,402246,Chongqing)

机构地区：[1]重庆师范大学计算机与信息科学学院,重庆401331 [2]重庆工程职业技术学院大数据与物联网学院,重庆402246

出　　处：《蚌埠学院学报》2024年第5期40-48,72,共10页Journal of Bengbu University

基　　金：重庆市教育委员会科学技术研究项目(KJQN202103413)。

摘　　要：针对现有图神经网络新闻推荐方法用户兴趣建模角度单一,且无法迅速拟合新节点特征的问题,提出一种全局图增强的图注意力网络模型,在以全局图采样子图的方式聚合邻居节点特征的同时,综合考虑用户历史时序特征和类别特征,多层级地建模用户兴趣。在MIND数据集上通过大量实验表明,提出的模型优于现有的基线网络方法。To address the limitations of existing graph neural network-based news recommendation methods,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 proposed 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 demonstrates the superiority of the proposed model over existing baseline network methods.

关 键 词：新闻推荐系统 图神经网络 全局图增强 用户兴趣建模 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.3[自动化与计算机技术—控制科学与工程]