Publisher
Springer Berlin Heidelberg
Reference24 articles.
1. Li, H.: Learning to Rank for Information Retrieval and Natural Language Processing. Morgan & Claypool Publishers (2011)
2. Ganjisaffar, Y., Caruana, R., Lopes, C.: Bagging gradient-boosted trees for high precision, low variance ranking models. In: SIGIR 2011 (2011)
3. Burges, C.: From RankNet to LambdaRank to LambdaMART: An overview. Technical Report MSR-TR-2010-82, Microsoft Research (2010)
4. Chapelle, O., Chang, Y., Liu, T.Y.: Future directions in learning to rank. In: JMLR: Workshop and Conference Proceedings 14 (2011)
5. Wang, L., Lin, J., Metzler, D.: A cascade ranking model for efficient ranked retrieval. In: SIGIR 2011 (2011)
Cited by
21 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information Retrieval;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10
2. ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information Retrieval;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18
3. Efficient and Effective Tree-based and Neural Learning to Rank;Foundations and Trends® in Information Retrieval;2023
4. Early Exit Strategies for Learning-to-Rank Cascades;IEEE Access;2023
5. ReNeuIR: Reaching Efficiency in Neural Information Retrieval;Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval;2022-07-06