A Developer Recommendation Method Based on Disentangled Graph Convolutional Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8073-4_44
Reference13 articles.
1. Cao, J., Lin, X., Cong, X., Ya, J., Liu, T., Wang, B.: DisenCDR: learning disentangled representations for cross-domain recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267–277 (2022)
2. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: International Conference on Research and Development in Information Retrieval, pp. 639–648 (2020)
3. Higgins, I., et al.: Towards a definition of disentangled representations. arXiv preprint arXiv:1812.02230 (2018)
4. Hu, L., Li, C., Shi, C., Yang, C., Shao, C.: Graph neural news recommendation with long-term and short-term interest modeling. Inf. Process. Manage. 57(2), 102142 (2020)
5. Jiang, H., et al.: DupHunter: detecting duplicate pull requests in fork-based development. IEEE Trans. Software Eng. 49(4), 2920–2940 (2023)
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