Abstract
The problem of sequential recommendation aims to use a user’s historical interaction sequence to predict and recommend the following item with which the user is most likely to interact. A range of outcomes has been attained in this field, from traditional methods to deep learning approaches. There are still two challenging problems with existing methods. First, users’ interaction sequences will always include noisy interest preferences. Additionally, most methods ignore the potential collaborative information among different features and cannot fully explore users’ true preferences. To solve these problems, we propose SR-MVG (Short for Sequential Recommendation based on Multi-View Graph Neural Networks) for sequential recommendation, which first transforms the user’s behavioral sequence into an item-item graph so that similar items are closely connected to clearly distinguish the core interests of users. Second, the user’s core preferences are retained adaptively by the designed multi-view attention network. In addition, we also designed a graph pooling strategy to reduce noise and extract more relevant user preferences. We carried out extensive tests on five public benchmarks, and the findings demonstrate that SR-MVG exhibits superior performance.
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References
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)
He, R., McAuley, J.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 191–200. IEEE (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
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
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)
You, J., Wang, Y., Pal, A., Eksombatchai, P., Rosenburg, C., Leskovec, J.: Hierarchical temporal convolutional networks for dynamic recommender systems. In: The World Wide Web Conference, pp. 2236–2246 (2019)
Shu, W., 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)
Zhang, M., Wu, S., Yu, X., Liu, Q., Wang, L.: Dynamic graph neural networks for sequential recommendation. IEEE Trans. Knowl. Data Eng. (2022)
Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., Wang, L.: Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Trans. Knowl. Data Eng. (2020)
Zhou, H., Tan, Q., Huang, X., Zhou, K., Wang, X.: Temporal augmented graph neural networks for session-based recommendations. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1798–1802 (2021)
Chen, H., Yeh, C.-C.M., Wang, F., Yang, H.: Graph neural transport networks with non-local attentions for recommender systems. In: Proceedings of the ACM Web Conference 2022, pp. 1955–1964 (2022)
Zheng, J., Ma, Q., Gu, H., Zheng, Z.: Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2338–2348 (2021)
Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 825–833 (2019)
Sun, F., e tal.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Li, Y., Chen, T., Zhang, P.-F., Yin, H.: Lightweight self-attentive sequential recommendation. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 967–977 (2021)
Fan, Z., et al.: Sequential recommendation via stochastic self-attention. In: Proceedings of the ACM Web Conference 2022, pp. 2036–2047 (2022)
Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Yu, Z., Lian, J., Mahmoody, A., Liu, G., Xie, X.: Adaptive user modeling with long and short-term preferences for personalized recommendation. In: IJCAI, pp. 4213–4219 (2019)
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487. PMLR (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5941–5948 (2019)
Acknowledgements
We are grateful for the support of the National Natural Science Foundation of Shandong ZR202011020044. We are grateful for the support of the National Natural Science Foundation of China 61772321. Natural Science Foundation of China 61772321.
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Wang, H., Liu, F., Zhang, X., Wang, L. (2023). Sequential Recommendation Based on Multi-View Graph Neural Networks. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_52
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