Abstract
Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-item interactions after that. However, there are still some unsatisfactory points for a CF model that GNNs could have done better. The way in which the collaborative signal are extracted through an implicit feedback matrix that is essentially built on top of the message-passing architecture of GNNs, and it only helps to update the embedding based on the value of the items (or users) embeddings neighboring. By identifying the similarity weight of users through their interaction history, a key concept of CF, we endeavor to build a user-user weighted connection graph based on their similarity weight.
In this study, we propose a recommendation framework, CombiGCN, in which item embeddings are only linearly propagated on the user-item interaction graph, while user embeddings are propagated simultaneously on both the user-user weighted connection graph and user-item interaction graph graphs with Light Graph Convolution (LGC) and combined in a simpler method by using the weighted sum of the embeddings for each layer. We also conducted experiments comparing CombiGCN with several state-of-the-art models on three real-world datasets.
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
Wang, X., He, X., Wang, M., Feng, F., Chua, T.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)
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)
Liao, J., et al.: SocialLGN: light graph convolution network for social recommendation. Inf. Sci. 589, 595–607 (2022)
Tran, T., Snasel, V.: Improvement graph convolution collaborative filtering with weighted addition input. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, T.P., Trawinski, B., Szczerbicki, E. (eds.) ACIIDS 2022. LNCS, vol. 13757, pp. 635–647. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21743-2_51
Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., Viet Hung, N.: Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference On Knowledge Discovery & Data Mining, pp. 2084–2092 (2021)
Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks (2017). https://arxiv.org/abs/1609.02907
Hamilton, W., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Berg, R., Kipf, T., Welling, M.: Graph convolutional matrix completion (2017). https://arxiv.org/abs/1706.02263
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2017). https://arxiv.org/abs/1412.6980
Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 93–102 (2012)
Tang, J., Liu, H., Gao, H., Das Sarmas, A.: eTrust: understanding trust evolution in an online world. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–261 (2012)
Yang, D., Qu, B., Yang, J., Cudre-Mauroux, P.: Revisiting user mobility and social relationships in LBSNs: a hypergraph embedding approach. In: The World Wide Web Conference, pp. 2147–2157 (2019)
Yang, D., Qu, B., Yang, J., Cudré-Mauroux, P.: LBSN2Vec++: heterogeneous hypergraph embedding for location-based social networks. IEEE Trans. Knowl. Data Eng. 34, 1843–1855 (2022)
Gao, Y., et al.: Self-guided learning to denoise for robust recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2022)
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
Nguyen, L.T., Tran, T.T. (2024). CombiGCN: An Effective GCN Model for Recommender System. In: Hà, M.H., Zhu, X., Thai, M.T. (eds) Computational Data and Social Networks. CSoNet 2023. Lecture Notes in Computer Science, vol 14479. Springer, Singapore. https://doi.org/10.1007/978-981-97-0669-3_11
Download citation
DOI: https://doi.org/10.1007/978-981-97-0669-3_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0668-6
Online ISBN: 978-981-97-0669-3
eBook Packages: Computer ScienceComputer Science (R0)