Learning-to-Rank with Nested Feedback
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56063-7_22
Reference45 articles.
1. Ai, Q., Bi, K., Luo, C., Guo, J., Croft, W.B.: Unbiased learning to rank with unbiased propensity estimation. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 385–394 (2018)
2. Ai, Q., Yang, T., Wang, H., Mao, J.: Unbiased learning to rank: online or offline? ACM Trans. Inf. Syst. (TOIS) 39(2), 1–29 (2021)
3. Bergstra, J., Yamins, D., Cox, D.D., et al.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, vol. 13, p. 20. Citeseer (2013)
4. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96 (2005)
5. Burges, C., Ragno, R., Le, Q.: Learning to rank with nonsmooth cost functions. In: Advances in Neural Information Processing Systems, vol. 19 (2006)
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