Research on the Application of Graph Neural Networks Based on Multiple Attention Mechanisms in Personalized Recommendation
DOI:
https://doi.org/10.13052/jwe1540-9589.2454Keywords:
Graph neural networks, personalized recommendation systems, attention mechanism, heterogeneous graphsAbstract
Graph neural networks (GNNs) have been widely applied due to their ability to model interactions among different objects. However, from the perspective of mathematical graph theory, the existing GNN frameworks still face challenges when dealing with specific graph structure problems. Nevertheless, the existing graph neural networks are unable to accurately identify and capture user characteristics based on common interests, have difficulty in flexibly handling diverse user interests and the differences in interests among users, and cannot effectively extract and utilize the feature information of intermediate nodes. To address these issues, this paper proposes a heterogeneous graph recommendation model based on a multi-level attention mechanism (MAHGRM-HGNN). The MAHGRM model consists of three major modules: the node-level aggregation and feature fusion module, the semantic-level aggregation module, and the importance analysis module. By introducing dual attention mechanisms at the node level and semantic level, MAHGRM can effectively identify and fuse multi-hop neighbor information related to user interests, while modeling the semantic features represented by different paths. Additionally, MAHGRM adopts an innovative feature fusion method, integrating intermediate heterogeneous nodes and their related different paths according to the topological structure of the graph, thereby avoiding the loss of intermediate node information and enriching the feature representation of the target node. In the importance analysis module, MAHGRM introduces a strategy for evaluating the importance of product nodes by calculating the importance scores of different product nodes, selecting the most popular products as the candidate set, and randomly selecting some products from it for recommendation to users. MAHGRM combines the node-level aggregation and feature fusion, semantic-level aggregation, and importance analysis modules closely. The key advantage lies in its ability to effectively integrate multi-level information in heterogeneous graphs and the collaborative optimization effect brought by the cross-module feature sharing mechanism, making the final recommendation results more targeted and timely. This cross-module collaborative effect ensures the precise capture of user interests and the efficiency of product recommendations, preventing the repetition of recommending the same product to users. The experimental results were extensively tested on multiple real-world datasets. The results showed that the performance of MAHGRM was significantly superior to that of the comparison models such as GCN, GAT, HAN, HPN, OSGNN and ie-HGCN. On the MovieLens-1M dataset, the AUC, ACC and F1-score of MAHGRM reached 0.931, 0.867 and 0.863 respectively, achieving the best performance and fully demonstrating the superiority of MAHGRM in terms of performance.
Downloads
References
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized Markov chains for next-basket recommendation,” in Proceedings of the 19th international conference on World wide web, 2010, pp. 811–820.
Y. Kiam Tan, X. Xu, and Y. Liu, “Improved recurrent neural networks for session-based recommendations,” arXiv e-prints, pp. arXiv–1606, 2016.
Y. Chen and Y. Tang, “Attentive capsule graph neural networks for session-based recommendation,” in International Conference on Knowledge Science, Engineering and Management. Springer, 2022, pp. 602–613.
Q. Liu, Y. Zeng, R. Mokhosi, and H. Zhang, “Stamp: short-term attention/memory priority model for session-based recommendation,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 1831–1839.
J. Song, H. Shen, Z. Ou, J. Zhang, T. Xiao, and S. Liang, “Islf: Interest shift and latent factors combination model for session-based recommendation.” in IJCAI, 2019, pp. 5765–5771.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 346–353.
Y. Chen, Q. Xiong, and Y. Guo, “Session-based recommendation: Learning multi-dimension interests via a multi-head attention graph neural network,” vol. 131. Elsevier, 2022, p. 109744.
F. Liu, J. Wang, and J. Yang, “A graph neural network recommendation method integrating multi head attention mechanism and improved gated recurrent unit algorithm,” Ieee Access, vol. 11, pp. 116 879–116 891, 2023.
J. Fang, B. Li, and M. Gao, “Collaborative filtering recommendation algorithm based on deep neural network fusion,” vol. 34, no. 2. Inderscience Publishers (IEL), 2020, pp. 71–80.
Z. Guo and H. Wang, “A deep graph neural network-based mechanism for social recommendations,” IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2776–2783, 2020.
R. Zhao, X. Xiong, X. Zu, S. Ju, Z. Li, and B. Li, “A hierarchical attention recommender system based on cross-domain social networks,” Complexity, vol. 2020, no. 1, p. 9071624, 2020.
G. Zhu, Y. Wang, J. Cao, Z. Bu, S. Yang, W. Liang, and J. Liu, “Neural attentive travel package recommendation via exploiting long-term and short-term behaviors,” Knowledge-Based Systems, vol. 211, p. 106511, 2021.
L. Huang, M. Fu, F. Li, H. Qu, Y. Liu, and W. Chen, “A deep reinforcement learning based long-term recommender system,” Knowledge-based systems, vol. 213, p. 106706, 2021.
H. Zhu, “Research on human resource recommendation algorithm based on machine learning,” Scientific Programming, vol. 2021, no. 1, p. 8387277, 2021.
Q. Yuan, “Retracted: Network education recommendation and teaching resource sharing based on improved neural network,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5511–5520, 2020.
L. Zheng, Z. Tianlong, H. Huijian, and Z. Caiming, “Personalized tag recommendation based on convolution feature and weighted random walk,” International Journal of Computational Intelligence Systems, vol. 13, no. 1, pp. 24–35, 2020.
Y. Zhao, H. Liu, and H. Duan, “Hgnn-gams: Heterogeneous graph neural networks for graph attribute mining and semantic fusion,” IEEE Access, 2024.
Y. Zhao, S. Xu, and H. Duan, “Hgnn- brfe: Heterogeneous graph neural network model based on region feature extraction,” Electronics, vol. 13, no. 22, p. 4447, 2024.
C. Xu, P. Zhao, Y. Liu, V. S. Sheng, J. Xu, F. Zhuang, J. Fang, and X. Zhou, “Graph contextualized self-attention network for session-based recommendation.” in IJCAI, vol. 19, no. 2019, 2019, pp. 3940–3946.
L. Dong, G. Zhu, Y. Wang, Y. Li, J. Duan, and M. Sun, “A graph positional attention network for session-based recommendation,” IEEE Access, vol. 11, pp. 7564–7573, 2023.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” in The world wide web conference, 2019, pp. 2022–2032.
Y. Yang, Z. Guan, J. Li, W. Zhao, J. Cui, and Q. Wang, “Interpretable and efficient heterogeneous graph convolutional network,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 2, pp. 1637–1650, 2021.
H. Ji, X. Wang, C. Shi, B. Wang, and P. S. Yu, “Heterogeneous graph propagation network,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 521–532, 2021.
Y. Yan, C. Li, Y. Yu, X. Li, and Z. Zhao, “Osgnn: Original graph and subgraph aggregated graph neural network,” Expert Systems with Applications, vol. 225, p. 120115, 2023.
H. Vaghari, M. Hosseinzadeh Aghdam, and H. Emami, “Group attention for collaborative filtering with sequential feedback and context aware attributes,” Scientific Reports, vol. 15, no. 1, p. 10050, 2025.
