Abstract
The click-through rate prediction of users is a critical task in the recommendation system. As a powerful machine learning method, graph neural networks have been favored by scholars to solve the task recently. However, most graph neural network-based click-through rate prediction models ignore the effectiveness of feature interaction and generally model all feature combinations, even if some are meaningless. Therefore, this paper proposes a Multi-head attention Graph Neural Network with Interactive Selection, named MGNN_IS in short, to capture the complex feature interactions via graph structures. In particular, there are three sub-graphs to be constructed to capture internal information of users and items respectively, and interactive information between users and items, namely the user internal graph, item internal graph, and user-item interaction graph correspondingly. Moreover, the proposed model designs a multi-head attention propagation module for the aggregation with an interactive selection strategy. This module can select the constructed graph and increase diversity with multiple heads to achieve the high-order interaction from the multiple layers. Finally, the proposed model fuses the features, and predicts. Experiments on three public datasets demonstrate that the proposed model outperformed other advanced models.
This work is supported by National Natural Science Foundation of China (Grant No.61902116).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Beck, D., Haffari, G., Cohn, T.: Graph-to-sequence learning using gated graph neural networks. arXiv preprint arXiv:1806.09835 (2018)
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10 (2016)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp. 639–648 (2020)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Li, Z., Cui, Z., Wu, S., Zhang, X., Wang, L.: FI-GNN: modeling feature interactions via graph neural networks for CTR prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 539–548 (2019)
Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.: 3D graph neural networks for RGBD semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5199–5208 (2017)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000. IEEE (2010)
Song, W., et al.: AutoInt: automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1161–1170 (2019)
Su, Y., Erfani, S.M., Zhang, R.: MMF: attribute interpretable collaborative filtering. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
Su, Y., Zhang, R.M., Erfani, S., Gan, J.: Neural graph matching based collaborative filtering. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 849–858 (2021)
Tao, Z., Wang, X., He, X., Huang, X., Chua, T.S.: HoAFM: a high-order attentive factorization machine for CTR prediction. Inf. Process. Manag. 57(6), 102076 (2020)
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, pp. 3307–3313 (2019)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019)
Wang, X., Wang, C.: Recommendation system of e-commerce based on improved collaborative filtering algorithm. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 332–335. IEEE (2017)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1059–1068 (2018)
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, S., Chen, J., Yao, M., Wu, X., Ge, Y., Li, S. (2024). Interactive Selection Recommendation Based on the Multi-head Attention Graph Neural Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8067-3_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8066-6
Online ISBN: 978-981-99-8067-3
eBook Packages: Computer ScienceComputer Science (R0)