计算机工程2025，Vol.51Issue(7)：100-110,11.DOI:10.19678/j.issn.1000-3428.0069099
基于二部图对比学习的特征增强推荐算法
Feature-enhanced Recommendation Algorithm Based on Bipartite Graph Contrastive Learning
摘要
Abstract
Recommendation algorithms are effective in addressing information overload,a common problem in the era of big data.Existing recommendation algorithms have different degrees of effectiveness but still face the challenge of learning higher quality items and user features to enhance recommendation performance.Therefore,this paper proposes a Feature-enhanced Recommendation algorithm based on Bipartite Graph Contrastive Learning(FRBGCL).An item feature initialization module is designed that can use Graph Convolutional Network(GCN)for the representation learning of bipartite graphs of all types of item relationships,and an attention mechanism-based feature fusion strategy is adopted to obtain the initial features of items.In addition,a graph Contrastive Learning(CL)module is designed based on the construction of user-item bipartite graphs,which can further enhance item and user features,leading to an improvement in recommendation performance.On three datasets,XuetangX,Last.fm,and Yelp2018,compared with the suboptimal algorithm,FRBGCL improves the Top20 recommendation results by 2.1％,6.8％,and 11.6％for recall;1.8％,6.1％,and 13.1％for Normalized Discounted Cumulative Gain(NDCG);and 1.7％,7.8％,and 8.4％for Hit Rate(HR),with optimal parameter selection.关键词
深度学习/推荐算法/二部图/对比学习/图卷积网络Key words
deep learning/recommendation algorithm/bipartite graph/Contrastive Learning(CL)/Graph Convolution Network(GCN)分类
信息技术与安全科学引用本文复制引用
余鹏,杨佳琦,陈欣然,贺超波..基于二部图对比学习的特征增强推荐算法[J].计算机工程,2025,51(7):100-110,11.基金项目
国家自然科学基金面上项目(62077045) （62077045）
广东省自然科学基金(2019A1515011292). （2019A1515011292）
