计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (5): 139-148.doi: 10.11896/jsjkx.240200078

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于特征网络对比学习的图协同过滤模型研究

吴鹏远, 方伟   

  1. 江南大学人工智能与计算机学院 江苏 无锡 214122
  • 收稿日期:2024-02-22 修回日期:2024-07-09 出版日期:2025-05-15 发布日期:2025-05-12
  • 通讯作者: 方伟(fangwei@jiangnan.edu.cn)
  • 作者简介:(6213113122@stu.jiangnan.edu.cn)

Study on Graph Collaborative Filtering Model Based on FeatureNet Contrastive Learning

WU Pengyuan, FANG Wei   

  1. School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2024-02-22 Revised:2024-07-09 Online:2025-05-15 Published:2025-05-12
  • About author:WU Pengyuan,born in 1999,master,is a member of CCF(No.R7041G).His main research interests include graph neural networks and recommendation algorithm.
    FANG Wei,born in 1980,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.39532M).His main research interests include evolutionary algorithm and swarm intelligence.

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摘要： 基于图协同过滤的推荐技术因能高效处理大规模交互数据而备受关注,但现实场景中的数据稀疏性问题限制了其有效性。对比学习应用于图协同过滤可以增强其在数据稀疏性上的性能,但现有方法通常通过随机抽样方式构建对比对,未能充分发掘对比学习在推荐系统中的潜力。为此,提出一种基于特征网络对比学习的图协同过滤模型(FeatureNet Contrastive Learning,FCL)。该模型通过计算特征向量之间的余弦相似度和概率归一化策略建立节点特征相似度矩阵,再使用对比学习对节点特征相似度矩阵进行影响力分析以捕捉节点间的高阶连接性,还引入随机扰动来增强模型的鲁棒性。在多个数据集上进行大量实验,验证了所提模型的有效性,尤其在处理高稀疏度数据集时,效果更为明显。

关键词: 推荐算法, 对比学习, 协同过滤, 图神经网络, 数据增强

Abstract: Graph-based collaborative filtering recommendation techniques have gained significant attention for their ability to efficiently process large-scale interaction data.However,the effectiveness of these techniques is limited by the sparsity of data in real-world scenarios.Recent research has started to apply contrastive learning to graph collaborative filtering to enhance its performance.Nonetheless,existing methods often construct contrastive pairs through random sampling,failing to fully explore the potential of contrastive learning in recommendation systems.To address these issues,this paper introduces a collaborative filtering model based on FeatureNet Contrastive Learning(FCL).The model establishes a node feature similarity matrix by computing the cosine similarity between feature vectors and applying a probabilistic normalization strategy.Using contrastive learning to perform influence analysis on the node feature similarity matrix,the model captures high-order connectivity between nodes,particularly demonstrating significant effectiveness in handling datasets with high sparsity.Extensive experiments conducted on multiple datasets prove the effectiveness of the proposed model.

Key words: Recommendation algorithm, Contrastive learning, Collaborative filtering, Graph neural networks, Data augmentation

中图分类号: 

  • TP183
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