计算机工程2026，Vol.52Issue(2)：89-100,12.DOI:10.19678/j.issn.1000-3428.0070127
基于双重图注意力网络生成子图的图神经协同推荐
Collaborative Recommendation Based on Graph Neural Network Clustering Subgraphs Using Dual Graph Attention Network
摘要
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.关键词
推荐系统/协同过滤/图神经网络/图注意力机制/子图生成Key words
recommendation system/Collaborative Filtering(CF)/Graph Neural Network(GNN)/graph attention mechanism/subgraph generation分类
信息技术与安全科学引用本文复制引用
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):89-100,12.基金项目
国家自然科学基金(52075316) （52075316）
上海市2021年度"科技创新行动计划"(21DZ1207502) （21DZ1207502）
国网浙江省电力有限公司杭州供电公司项目(5211HZ17000F). （5211HZ17000F）
