A Session Recommendation Model Based on Heterogeneous Graph Neural Network
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40289-0_13
Reference27 articles.
1. Wang, Y., Zhao, Y., Zhang, Y., Derr, T.: Collaboration-aware graph convolutional network for recommender systems. In: Proceedings of the ACM Web Conference 2023, pp. 91–101 (2023)
2. Vančura, V., Alves, R., Kasalick`y, P., Kordík, P.: Scalable linear shallow autoencoder for collaborative filtering. In: Proceedings of the 16th ACM Conference onRecommender Systems, pp. 604–609 (2022)
3. Su, J., Chen, C., Liu, W., Wu, F., Zheng, X., Lyu, H.: Enhancing hierarchy-aware graph networks with deep dual clustering for session-based recommendation. In: Proceedings of the ACM Web Conference 2023, pp. 165–176 (2023)
4. He, B., He, X., Zhang, Y., Tang, R., Ma, C.: Dynamically expandable graph convolution for streaming recommendation. arXiv preprint arXiv:2303.11700 (2023)
5. Chen, Y., Liu, Z., Li, J., McAuley, J., Xiong, C.: Intent contrastive learning forsequential recommendation. In: Proceedings of the ACM Web Conference 2022, pp. 2172–2182 (2022)
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