[1]ZHU Jinxia,MENG Xiangfu,XING Changzheng,et al.Collaborative filtering recommendation approach fused with graph convolutional attention mechanism[J].CAAI Transactions on Intelligent Systems,2023,18(6):1295-1304.[doi:10.11992/tis.202203039]
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Collaborative filtering recommendation approach fused with graph convolutional attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1295-1304 Column: 学术论文—人工智能基础 Public date: 2023-11-05
Title:
Collaborative filtering recommendation approach fused with graph convolutional attention mechanism
Author(s):
ZHU Jinxia;  MENG Xiangfu;  XING Changzheng;  ZHANG Xiaoyan
School of Electronics and Information Engineering, Liaoning Technical University, Huludao 125105, China
Keywords:
graph embedding technology; graph convolutional network; attention mechanism; collaborative filtering; user preference; collaborative filtering; high-order interaction; neighbor aggregation
CLC:
TP311
DOI:
10.11992/tis.202203039
Abstract:
The graph convolutional neural network (GCN) has attracted extensive attention due to its powerful modeling capabilities. In item recommendation, existing graph convolution collaborative filtering techniques ignore the importance of neighbor nodes in the propagation aggregation process, making the embedding vector representation of user and item unreasonable. Therefore, this paper proposes a collaborative filtering recommendation model fused with graph convolutional attention to address this problem. First, user-item interaction information was mapped to a low-dimensional, dense vector space using graph embedding techniques. Further, the high-order interaction information between the user and the item was learned using stacking multiple layers of GCN. The model also fused attention mechanisms to adaptively assign weights to neighbor nodes, thereby capturing the influence of highly representative neighbors. Simultaneously, the model could rely only on feature expressions between nodes when aggregating feature information from neighboring nodes, increasing the independence of the graph structure and improving the generalization capability of the model. Finally, a hierarchical aggregation function that aggregated multiple embedding vectors, which was learned from the graph convolution layer by weighting, was designed, and the inner product function was used to obtain the association score between the user and the item. Results of the extensive experiments conducted on three real datasets have demonstrated the effectiveness of the proposed approach.
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