Publisher
Springer Nature Switzerland
Reference49 articles.
1. Ai, Q., Bi, K., Guo, J., Croft, W.B.: Learning a deep listwise context model for ranking refinement. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 135–144. SIGIR ’18, Association for Computing Machinery, New York, NY, USA (2018)
2. Arik, S.O., Pfister, T.: TabNet: attentive interpretable tabular learning. Proc. AAAI Conf. Artif. Intell. 35(8), 6679–6687 (2021)
3. Bruch, S., Han, S., Bendersky, M., Najork, M.: A stochastic treatment of learning to rank scoring functions. In: Proceedings of the 13th WSDM, pp. 61–69 (2020)
4. Bruch, S., Lucchese, C., Nardini, F.M.: Efficient and effective tree-based and neural learning to rank. Found. Trends® Inf. Retrieval 17(1), 1–123 (2023)
5. Bruch, S., Zoghi, M., Bendersky, M., Najork, M.: Revisiting approximate metric optimization in the age of deep neural networks. In: Proceedings of the 42nd SIGIR, pp. 1241–1244 (2019)