Abstract
Recently, Graph Neural Network (GNN) has been proved to be an efficient technique to solve the problem of graph-structured data, and many graph-based methods of recommendation have shown noticeably good performances. However, many approaches use pure GNN layers as the encoder of the nodes, which we think may limit the performance of the model. In this paper, we propose Fusion Graph Convolutional Collaborative Filtering (FGC-CF) which uses DeepWalk and graph convolutional layers to be the encoder of nodes to enhance the capability of the node encoder. For better modeling the similarity of user and item, we involve the local inference of the ESIM [3] to obtain the user representations by considering the interacted items, and the item representations by considering the interacted users. We conduct experiments on four datasets and the results not only show the remarkable performance of FGC-CF but also prove the necessity of using DeepWalk and ESIM local inference technologies.
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
References
Berg, R.V.D., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)
Bonet, E.R., Nguyen, D.M., Deligiannis, N.: Temporal collaborative filtering with graph convolutional neural networks. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4736–4742. IEEE (2021)
Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038 (2016)
Yao, G., Songchan, C.: Graph convolutional network for recommender systems. J. Softw. 031(004), 1101–1112 (2020)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Lee, J., Kim, S., Lebanon, G., Singer, Y.: Local low-rank matrix approximation. In: International Conference on Machine Learning, pp. 82–90. PMLR (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. Adv. Neural Inform. Process. Syst. 20, 1257–1264 (2007)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp. 111–112 (2015)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
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, R., Shi, C., Song, G., Li, Q.: Multi-component graph convolutional collaborative filtering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6267–6274 (2020)
Zheng, Y., Tang, B., Ding, W., Zhou, H.: A neural autoregressive approach to collaborative filtering. In: International Conference on Machine Learning, pp. 764–773. PMLR (2016)
Acknowledgments
We thank the anonymous reviewers for their contribution to the publication of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Liu, Y., Sun, F. (2021). Fusion Graph Convolutional Collaborative Filtering. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-89363-7_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89362-0
Online ISBN: 978-3-030-89363-7
eBook Packages: Computer ScienceComputer Science (R0)