基于自监督的多视角图协同过滤推荐方法

doi:10.19678/j.issn.1000-3428.0067851

摘要/Abstract

摘要： 现有的图协同过滤算法在现实场景中存在数据稀疏问题,同时在相邻信息聚合的过程中使得特征学习更容易受到交互噪声的影响。为了解决上述问题,提出一个基于自监督的多视角图协同过滤(SMGCF)推荐方法,通过图神经网络学习用户和项目节点的嵌入表示。在学习节点嵌入表示的过程中,考虑到单个节点间的交互关系以及聚类节点间的聚类关系对推荐结果的影响,引入自监督学习来辅助图协同过滤算法进行多视角关系的挖掘。针对节点交互级关系视角,通过数据增强得到多个用户-项目交互二分图,并且提出一种节点交互级关系的对比学习方法;针对节点聚类级关系视角,提出一种节点聚类级关系的对比学习方法。通过多视角融合策略将2种类型的对比学习方法进行融合,从而提升节点嵌入效果。在4个公开的数据集上进行实验,实验结果证明了SMGCF的可行性和有效性。相比最优基准方法NCL,SMGCF在Recall@10和NDCG@10指标上最高可提升2.1%和4.3%。

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

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.

Key words: supervised learning, recommendation method, data augmentation, Graph Neural Network(GNN), contrastive learning

中图分类号:

TP18

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.

ZHANG Baoxin, YANG Dan, NIE Tiezheng, KOU Yue. Recommendation Method Based on Self-supervised Multi-view Graph Collaborative Filtering[J]. Computer Engineering, 2024, 50(5): 100-110.

https://www.ecice06.com/CN/Y2024/V50/I5/100

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