Abstract
To capture complex interaction semantics beyond pairwise relationships for social recommendation, a novel recommendation model, namely Motif-SocialRec, is proposed under the perspective of motif. It efficiently describes interaction pattern from multi-channel with different motifs. In the model, we depict a series of local structures by motif, which can describe the high-level interactive semantics in the fused network from three views. By employing hypergraph convolution network, representations that preserve potential semantic patterns can be learned. Additionally, we enhance the learned representations by establishing self-supervised learning tasks on different scales to further explore the inherent characteristics of the network. Finally, a joint optimization model is constructed by integrating the primary and auxiliary tasks to produce recommendation predictions. Results of extensive experiments on four real-world datasets show that Motif-SocialRec significantly outperforms baselines in terms of different evaluation metrics.
Supported by organization x.
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
Tang, J., Hu, X., Gao, H., et al.: Exploiting local and global social context for recommendation. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 2712–2718. World Scientific, Chiyoda City, Tokyo (2013)
Gao, C., Zheng, Y., Li, N., et al.: A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Trans. Recommender Syst. 1(1), 1–51 (2023)
Wang, X., He, X., Wang, M., et al.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174. Association for Computing Machinery, New York, NY, United States (2019)
Wu, L., Sun, P., Fu, Y., et al.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235–244. Association for Computing Machinery, New York, NY, United States (2019)
He, X., Deng, K., Wang, X., et al.: 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. Association for Computing Machinery, New York, NY, United States (2020)
Liu, C., Li, Y., Lin, H., et al.: GNNRec: gated graph neural network for session-based social recommendation model. J. Intell. Inf. Syst. 60(1), 137–156 (2023)
Bai, T., Zhang, Y., Wu, B., et al.: Temporal graph neural networks for social recommendation. In:2020 IEEE International Conference on Big Data, pp. 898–903. Institute of Electrical and Electronics Engineers, Piscataway, NJ (2020)
Wu, L., Li, J., Sun, P., et al.: Diffnet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans. Knowl. Data Eng. 34(10), 4753–4766 (2020)
Wei, C., Fan, Y., Zhang, J.: Time-aware service recommendation with social-powered graph hierarchical attention network. IEEE Trans. Serv. Comput. 16(3), 2229–2240 (2022)
Song, W., Xiao, Z., Wang, Y., et al.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555–563. Association for Computing Machinery, New York, NY, United States (2019)
Hoang, T.L., Pham, T.D., Ta, V.C.: Improving graph convolutional networks with transformer layer in social-based items recommendation. In: Proceedings of the 13th International Conference on Knowledge and Systems Engineering, pp. 1–6. Institute of Electrical and Electronics Engineers, Piscataway, NJ (2021)
Zhang, Y., Huang, J., Li, M., et al.: Contrastive graph learning for social recommendation. Front. Phys. 10, 35 (2022)
Wu, J., Fan, W., Chen, J., et al.: Disentangled contrastive learning for social recommendation. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 4570–4574. Association for Computing Machinery New York, NY, United States (2022)
Milo, R., Shen-Orr, S., Itzkovitz, S., et al.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Yu, J., Yin, H., Li, J., et al.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the Web Conference, pp. 413–424. Association for Computing Machinery, New York, NY, United States (2021)
Yu, J., Yin, H., Gao, M., et al.: Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2084–2092. Association for Computing Machinery, New York, NY, United States (2021)
Zhao, H., Xu, X., Song, Y., et al.: Ranking users in social networks with motif-based pagerank. IEEE Trans. Knowl. Data Eng. 33(5), 2179–2192 (2019)
Feng, Y., You, H., Zhang, Z., et al.: Hypergraph neural networks. In: Proceedings of the 33rd Association for the Advancement of Artificial Intelligence, vol. 33, pp. 3558–3565. (2019)
Rendle, S., Freudenthaler, C., Gantner, Z., et al.: BPR: Bayesian personalized ranking from Implicit Feedback. UAI, 452–461 (2012)
Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 261–270. Association for Computing Machinery, New York, NY, United States (2014)
Acknowledgements
The authors are very grateful to the anonymous reviewers and editors. Their helpful comments and constructive suggestions helped us to significantly improve this work. We also wish to thank the authors of the compared algorithms for sharing their codes. This work was supported by the National Natural Science Foundation of China (U21A20513, 62076154, 62022052), and the Key R &D Program of Shanxi Province (202202020101003).
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
Du, H., Liu, Y., Wang, W., Bai, L. (2024). Motif-SocialRec: A Multi-channel Interactive Semantic Extraction Model for Social Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-8082-6_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8081-9
Online ISBN: 978-981-99-8082-6
eBook Packages: Computer ScienceComputer Science (R0)