软件导刊2024，Vol.23Issue(10)：129-138,10.DOI:10.11907/rjdk.232064
基于图神经网络的挖掘潜在偏好图推荐算法
Recommendation Algorithm for Mining Potential Preference Graph Based on Graph Neural Network
摘要
Abstract
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.关键词
推荐系统/知识图谱/图神经网络/深度学习Key words
recommender system/knowledge graph/graph neural network/deep learning分类
信息技术与安全科学引用本文复制引用
方霖枫,周仁杰..基于图神经网络的挖掘潜在偏好图推荐算法[J].软件导刊,2024,23(10):129-138,10.基金项目
国家重点研发计划项目(2022YFB3105401) （2022YFB3105401）
