Adaptive Augmentation and Neighbor Contrastive Learning for Multi-Behavior Recommendation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-7235-3_2
Reference16 articles.
1. Wei, W., Huang, C., Xia, L., Xu, Y., Zhao, J., Yin, D.: Contrastive meta learning with behavior multiplicity for recommendation. In: WSDM 2022: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event/Tempe, AZ, USA, 21–25 February 2022, pp. 1120–1128 (2022)
2. Guo, W., et al.: Compressed interaction graph based framework for multi-behavior recommendation. In: Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April– 4 May 2023, pp. 960–970 (2023)
3. Yan, M., et al.: Cascading residual graph convolutional network for multi-behavior recommendation. ACM Trans. Inf. Syst. 42, 10:1–10:26 (2024)
4. Zhang, Yet al.: Enhancing sequential recommendation with graph contrastive learning. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23–29 July 2022, pp. 2398–2405 (2022)
5. Xu, J., et al.: Multi-behavior self-supervised learning for recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, 23–27 July 2023, pp. 496–505 (2023)
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