Enhanced Attention-Based Graph Convolutional Networks: Boosting Collaborative Filtering Recommendations

• Yu Zhang,
• Qianhui Ding,
• Yilin Su,
• Yahan Feng,

Vol. 20, No. 2, February 28, 2026
10.3837/tiis.2026.02.002, Download Paper (Free):

• Graph Convolutional Network
• Recommendation System
• Collaborative Filtering
• Graph Attention Network

Abstract

Recommendation algorithms can filter personalized information from massive data, effectively alleviating the current social problem of information overload. This study introduces a graph convolutional network with enhanced capabilities collaborative filtering recommendation model combined with graph attention (Enhanced Attention-based GCN, EAGCN), aiming to address the shortcomings of existing graph convolutional network-based recommendation models, such as the inability to effectively distinguish the importance of adjacent nodes during node information aggregation. To mitigate the over-smoothing issue, a residual-enhanced graph convolutional network collaborative filtering recommendation method (R-EAGCN) is designed based on the original model. Comparative experiments between EAGCN, R-EAGCN and the best baseline models show that their recall rate and NDCG metrics outperform other baseline algorithms. Specifically, the recall rates of EAGCN and R-EAGCN are increased by nearly 10% and 15% respectively compared to the five baseline models.

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