Collaborative Recommendation Based on Graph Neural Network Clustering Subgraphs Using Dual Graph Attention Network

doi:10.19678/j.issn.1000-3428.0070127

Abstract

Abstract: A recommendation system based on Graph Neural Network (GNN) can extract high-order connectivity between users and items. Collaborative Filtering (CF) is a classic recommendation algorithm that suffers from over-smoothing issues during the stacking of multilayer graph convolutional layers owing to the similarity between user and item embeddings. To address this issue, a graph neural network collaborative filtering recommendation algorithm named DAC-GCN that generates subgraphs using a dual graph attention mechanism is proposed. Users with common interests are clustered to generate subgraphs to avoid spreading negative information from high-order neighbors to the embedding learning. The graph attention mechanism is used in advance to preprocess node embeddings, increasing attention to important nodes and improving subgraph generation results. In addition, the graph attention mechanism is reintroduced during the subgraph propagation process to enhance the node discrimination within the subgraph, thereby improving the propagation of embedded information within the subgraph, reducing the impact of over-smoothing, and enhancing the recommendation performance. Finally, the proposed algorithm is tested on three publicly available datasets using Normalized Discounted Cumulative Gain (NDCG) and recall as evaluation metrics. The experimental results validate the effectiveness and superiority of the proposed algorithm.

Key words: recommendation system, Collaborative Filtering (CF), Graph Neural Network (GNN), graph attention mechanism, subgraph generation

摘要： 基于图神经网络(GNN)的推荐系统可以提取用户与项目之间的高阶连通性。协同过滤(CF)是一种经典的推荐算法,在进行多层图卷积堆叠的过程中,由于用户和项目的嵌入会变得相似,导致出现过平滑问题。针对这一问题,提出一种采用双重图注意力机制生成子图的图神经网络协同过滤推荐算法(DAC-GCN)。将具有共同兴趣的用户聚类生成子图,以避免将高阶邻居的负面信息传播到嵌入学习中,并预先采用图注意力机制对节点嵌入进行预处理,提升对重要节点的关注度,以改善子图生成结果。另外,在子图传播过程中再次引入图注意力机制,强化子图内的节点区分度,从而改善子图内嵌入信息的传播,降低过平滑的影响,提升推荐效果。最后,以3个公开的数据集为测试对象,以归一化折损累积增益(NDCG)与召回率为评估指标,对所提算法进行测试,实验结果验证了该算法的有效性和优越性。

CLC Number:

TP391

XUE Yang, QIN Yao, ZHANG Shuxiang. Collaborative Recommendation Based on Graph Neural Network Clustering Subgraphs Using Dual Graph Attention Network[J]. Computer Engineering, 2026, 52(2): 89-100.

薛阳, 秦瑶, 张舒翔. 基于双重图注意力网络生成子图的图神经协同推荐[J]. 计算机工程, 2026, 52(2): 89-100.

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