Abstract
With the increasing number of data appears in the form of session, it shows great importance to predict the future items based on the present ones in the session. By now, great progress has been made in the Graph Neural Network to build the session-based recommendation system. Nevertheless, the existing method of session-data modeling through the graph neural network ignores the degree of nodes which to some extent reflects the importance of the nodes in the graph. Intuitively, the possibility of the item to be clicked increases along with the degree of the node represents this item. Inspired by the aforementioned observation, we analyze the session data and propose to use the degree information of the nodes in the session graph to improve the effect of session recommendation. The experiments show that the proposed method outperforms the current mainstream approaches on a number of real-world data sets, such as Tmall and Diginetica.
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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)
Hidasi, B., Karatzoglou, A., Baltrunas, L., et al.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Quadrana, M., Karatzoglou, A., Hidasi, B., et al.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137 (2017)
Bogina, V., Kuflik, T.: Incorporating dwell time in session-based recommendations with recurrent neural networks. In: RecTemp@ RecSys, pp. 57–59 (2017)
Wu, S., Tang, Y., Zhu, Y., et al.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 346–353 (2019)
Li, Y., Tarlow, D., Brockschmidt, M., et al.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Xu, C., Zhao, P., Liu, Y., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol. 19, pp. 3940–3946 (2019)
Yu, F., Zhu, Y., Liu, Q., et al.: TAGNN: target attentive graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1921–1924 (2020)
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Li, J., Ren, P., Chen, Z., et al.: Neural attentive session-based recommendation. In: 2017 Proceedings of the ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Liu, Q., Zeng, Y., Mokhosi, R., et al.: STAMP: short-term attention memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1831–1839 (2018)
Acknowledgments
The work is supported by the National Natural Science Foundation of China No. 61872062, the National High Technology Research and Development Program of China (No. 2018YFB1005100, 2018YFB1005104), special fund project of science and technology incubation and achievement transformation in Neijiang City (No. 2019KJFH005).
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Huang, X., He, Y., Yan, B., Zeng, W. (2021). Fusing the Degree of Nodes in the Session Graph for Session-Based Recommendation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_84
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DOI: https://doi.org/10.1007/978-3-030-92310-5_84
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