Abstract
Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks. However, previous works usually focus on modeling users, not much attention is paid to items. Moreover, the possible changes in the attraction of items over time, which is like the dynamic interest of users are rarely considered, and neither do the correlations among items. To overcome these limitations, this paper proposes graph neural networks with dynamic and static representations for social recommendation (GNN-DSR), which considers both dynamic and static representations of users and items and incorporates their relational influence. GNN-DSR models the short-term dynamic and long-term static interactional representations of the user’s interest and the item’s attraction, respectively. The attention mechanism is used to aggregate the social influence of users on the target user and the correlative items’ influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GNN-DSR.
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
Notes
- 1.
Ciao and Epinions available from http://www.cse.msu.edu/~tangjili/trust.html.
- 2.
Delicious available from https://grouplens.org/datasets/hetrec-2011/.
References
Fan, W., Li, Q., Cheng, M.: Deep modeling of social relations for recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 8075–8076 (2018)
Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)
Fan, W., et al.: A graph neural network framework for social recommendations. IEEE Trans. Knowl. Data Eng. (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: The 4th ACM Conference on Recommender Systems, pp. 135–142 (2010)
Li, J., et al.: Neural attentive session-based recommendation. In: The 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Lin, J., Chen, S., Wang, J.: Graph neural networks with dynamic and static representations for social recommendation. arXiv preprint arXiv:2201.10751 (2022)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: The ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Soc. 27(1), 415–444 (2001)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: The 10th International Conference on World Wide Web, pp. 285–295 (2001)
Song, W., et al.: Session-based social recommendation via dynamic graph attention networks. In: The 12th ACM International Conference on Web Search and Data Mining, pp. 555–563 (2019)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013). https://doi.org/10.1007/s13278-013-0141-9
Tieleman, T., Hinton, G., et al.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2), 26–31 (2012)
Veličković, P., et al.: Graph attention networks. In: International Conference on Learning Representations (2018)
Wu, S., Zhang, W., Sun, F., Cui, B.: Graph neural networks in recommender systems: a survey. arXiv preprint arXiv:2011.02260 (2020)
Acknowledgements
This work is supported by the National Key R&D Program of China (2018AAA0101203), and the National Natural Science Foundation of China (62072483).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, J., Chen, S., Wang, J. (2022). Graph Neural Networks with Dynamic and Static Representations for Social Recommendation. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-00126-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00125-3
Online ISBN: 978-3-031-00126-0
eBook Packages: Computer ScienceComputer Science (R0)