计算机工程2024，Vol.50Issue(5)：100-110,11.DOI:10.19678/j.issn.1000-3428.0067851
基于自监督的多视角图协同过滤推荐方法
Recommendation Method Based on Self-supervised Multi-view Graph Collaborative Filtering
摘要
Abstract
Existing graph collaborative filtering algorithms suffer from data sparsity in real-world scenarios and make feature learning more susceptible to interaction noise when aggregating adjacent information.To address these issues,a recommendation method based on Self-supervised Multi-view Graph Collaborative Filtering(SMGCF)is proposed.The SMGCF learns the embedded representations of the user and item nodes by using Graph Neural Network(GNN).In the process of learning the embedding representation of nodes,self-supervised learning is introduced to assist the graph collaborative filtering algorithm in mining relationships from multiple views,considering the influence of the interaction relationships between individual nodes and the clustering relationships between clustered nodes on the recommendation results.For the node-interaction-level relationship view,multiple user-item interaction bipartite graphs are obtained by data augmentation,and a contrastive learning method for node-interaction-level relationships is proposed.For the node-clustering-level relationship view,a contrastive learning method for node-clustering-level relationships is proposed.The node-embedding effect is enhanced by fusing the two types of contrast learning methods through a multi-view integration strategy.Experiments are conducted using four public datasets.The experimental results demonstrate the feasibility and effectiveness of the SMGCF.Compared with the best-performing baseline method,NCL and SMGCF achieved the highest improvements of 2.1％in Recall@10 metric and 4.3％in NDCG@10 metric.关键词
自监督学习/推荐方法/数据增强/图神经网络/对比学习Key words
supervised learning/recommendation method/data augmentation/Graph Neural Network(GNN)/contrastive learning分类
信息技术与安全科学引用本文复制引用
张宝鑫,杨丹,聂铁铮,寇月..基于自监督的多视角图协同过滤推荐方法[J].计算机工程,2024,50(5):100-110,11.基金项目
国家自然科学基金(62072084,62072086) （62072084,62072086）
辽宁省教育厅科学研究项目(LJKMZ20220646). （LJKMZ20220646）
