Abstract
Many practical recommender systems recommend personalized items for different users by mining user-item interaction sequences. The interaction sequences, as a whole, imply the manifold collaborative relations among users and items. Further, from the view of users, the item orders and time intervals between interactions could expose the evolution of user interests, and from the view of items, attributes of the items on interaction sequences may reveal the variation of item popularity. However, most of the existing recommendation models ignore those valuable information, and cannot fully explore the intrinsic implication of interaction sequences. In the paper, we propose a method named Sirius, which develops GNNs (Graph Neural Networks) to model the collaborative relations and capture the dynamics of time and attribute features in sequences. We give the workflow of the Sirius method, and describe the implementations about graph construction, item embedding generation, sequence embedding generation and next-item prediction. Finally, we give an example of Sirius recommendations, which visually shows the impact of feature information on the recommendation results. At present, Sirius has been adopted by MX Player, one of India’s largest streaming platforms, recommending movies for thousands of users.
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
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Gupta, P., Garg, D., Malhotra, P., Vig, L., Shroff, G.: Niser: Normalized item and session representations with graph neural networks. arXiv preprint arXiv:1909.04276 (2019)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 843–852 (2018)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings (2016)
Hosseinzadeh Aghdam, M., Hariri, N., Mobasher, B., Burke, R.: Adapting recommendations to contextual changes using hierarchical hidden Markov models. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 241–244 (2015)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Li, J., Wang, Y., McAuley, J.: Time interval aware self-attention for sequential recommendation. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 322–330 (2020)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Li, R., Shen, Y., Zhu, Y.: Next point-of-interest recommendation with temporal and multi-level context attention. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1110–1115. IEEE (2018)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings (2016)
Pan, Z., Cai, F., Chen, W., Chen, H., de Rijke, M.: Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1195–1204 (2020)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 579–588 (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)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. CoRR (2017)
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, pp. 346–353 (2019)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp. 3940–3946 (2019)
Ye, W., Wang, S., Chen, X., Wang, X., Qin, Z., Yin, D.: Time matters: sequential recommendation with complex temporal information. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1459–1468 (2020)
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 62072450 and the 2019 joint project with MX Media.
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
Dong, X., Jin, B., Zhuo, W., Li, B., Xue, T. (2021). Sirius: Sequential Recommendation with Feature Augmented Graph Neural Networks. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-73200-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73199-1
Online ISBN: 978-3-030-73200-4
eBook Packages: Computer ScienceComputer Science (R0)