Abstract
Sequential recommendation aims to suggest items to users based on sequential dependencies. Graph neural networks (GNNs) are recently proposed to capture transitions of items by treating session sequences as graph-structured data. However, existing graph construction approaches mainly focus on the directional dependency of items and ignore benefits of feature aggregation from undirectional relationship. In this paper, we innovatively propose a joint graph contextualized network (JGCN) for sequential recommendation, which constructs both the directed graphs and undirected graphs to jointly capture current interests and global preferences. Specifically, we introduce gate graph neural networks and model the combined embedding of weighted position and node information from directed graphs for capturing current interests. Besides, to learn global preferences, we propose a graph collaborative attention network with correlation-based similarity of items from undirected graphs. Finally, a feed-forward layer with the residual connection is applied to synthetically obtain accurate transitions of items. Extensive experiments conducted on three datasets show that JGCN outperforms state-of-the-art methods.
Supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61672498.
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, S., Hu, L., Cao, L., Orgun, M.: Sequential recommender systems: challenges, progress and prospects. In: Proceedings of International Joint Conferences on Artificial Intelligence (2019)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the International Conference on Learning Representations (2016)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. (TNNLS) 32, 1–21 (2020)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 346–353. AAAI (2019)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: International Joint Conferences on Artificial Intelligence, vol. 19, pp. 3940–3946 (2019)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. In: Proceedings of 4th International Conference on Learning Representations (2016)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: Proceedings of International Conference on Learning Representations (2018)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (2009)
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
Huang, R., Han, C., Cui, L. (2021). Joint Graph Contextualized Network for Sequential Recommendation. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-86365-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86364-7
Online ISBN: 978-3-030-86365-4
eBook Packages: Computer ScienceComputer Science (R0)