Graphical contrastive learning for multi-interest sequential recommendation
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Published:2025-01
Issue:
Volume:259
Page:125285
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ISSN:0957-4174
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Container-title:Expert Systems with Applications
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language:en
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Short-container-title:Expert Systems with Applications
Author:
Liang ShunpanORCID, Kong QianjinORCID, Lei YuORCID, Li ChenORCID
Funder
Yanshan University
Reference48 articles.
1. Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., & Tang, J. (2020). Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2942–2951). 2. Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., et al. (2021). Sequential recommendation with graph neural networks. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 378–387). 3. Chen, Y., Liu, Z., Li, J., McAuley, J., & Xiong, C. (2022). Intent contrastive learning for sequential recommendation. In Proceedings of the ACM web conference 2022 (pp. 2172–2182). 4. Iterative deep graph learning for graph neural networks: Better and robust node embeddings;Chen;Advances in Neural Information Processing Systems (NIPS),2020 5. Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., et al. (2018). Sequential recommendation with user memory networks. In Proceedings of the 8th ACM international conference on web search and data mining (pp. 108–116).
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