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Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval.
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
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Direct maximization of rank-based metrics for information retrieval
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Interpretable Learning-to-Rank with Generalized Additive Models
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AdaRank: A Boosting Algorithm for Information Retrieval. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . 391–398
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An Experimental Comparison of Click Position-bias Models. In Proceedings of the 2008 International Conference on Web Search and Data Mining . 87–94
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SoftRank: Optimizing Non-smooth Rank Metrics. In Proceedings of the 1st International Conference on Web Search and Data Mining . 77–86
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008 · 2008
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Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th International Conference on Machine Learning . 1192–1199
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008 · 2008
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Directly Optimizing Evaluation Measures in Learning to Rank. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . 107–114
Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. 2008 · 2008
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Expected Reciprocal Rank for Graded Relevance. In Proceedings of the 18th ACM Conference on Information and Knowledge Management . 621–630
Olivier Chapelle, Donald Metzler, Ya Zhang, and Pierre Grinspan. 2009 · 2009
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Yahoo! Learning to Rank Challenge Overview. 1–24
Olivier Chapelle and Yi Chang. 2011 · 2011
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On NDCG Consistency of Listwise Ranking Methods. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics , Vol. 15. PMLR, 618–626
Pradeep Ravikumar, Ambuj Tewari, and Eunho Yang. 2011 · 2011
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Introducing LETOR 4.0 Datasets
Tao Qin and Tie-Yan Liu. 2013 · 2013
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Position-Aware ListMLE: A Sequential Learning Process for Ranking. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence . 449–458
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Generalization Error Bounds for Learning to Rank: Does the Length of Document Lists Matter?. In Proceedings of the 32nd International Conference on Machine Learning . 315–323
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Generalization Analysis of Listwise Learning-to-rank Algorithms. In Proceedings of the 26th Annual International Conference on Machine Learning . 577–584
Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. 2009 · 2009
Cited alongside, same era.
Learning to rank for information retrieval
Tie-Yan Liu. 2009 · 2009
Cited alongside, same era.
The P-Norm Push: A Simple Convex Ranking Algorithm That Concentrates at the Top of the List
Cynthia Rudin. 2009 · 2009
Cited alongside, same era.
From RankNet to LambdaRank to LambdaMART: An Overview
Christopher J.C. Burges. 2010 · 2010
Cited alongside, same era.
Gradient Descent Optimization of Smoothed Information Retrieval Metrics
Olivier Chapelle and Mingrui Wu. 2010 · 2010
Cited alongside, same era.
A general approximation framework for direct optimization of information retrieval measures
Tao Qin, Tie-Yan Liu, and Hang Li. 2010 · 2010
Cited alongside, same era.
Adapting boosting for information retrieval measures
Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. 2010 · 2010
Cited alongside, same era.
Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Sebastian Bruch, Masrour Zoghi, Mike Bendersky, and Marc Najork. 2019b
Cited in the paper.
Ambuj Tewari and Sougata Chaudhuri. 2015 · 2015
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Unbiased Learning-to-Rank with Biased Feedback. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining . 781–789
Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017 · 2017
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017 · 2017
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Direct Learning to Rank and Rerank. In Proceedings of Artificial Intelligence and Statistics AISTATS
Cynthia Rudin and Yining Wang. 2018 · 2018
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The LambdaLoss Framework for Ranking Metric Optimization. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 1313–1322
Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018 · 2018
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An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance. In Proceedings of the 2019 ACM SIGIR International Conference on the Theory of Information Retrieval
Sebastian Bruch, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2019a · 2019
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A Stochastic Treatment of Learning to Rank Scoring Functions. In Proceedings of the 13th International Conference on Web Search and Data Mining . 61–69
Sebastian Bruch, Shuguang Han, Michael Bendersky, and Marc Najork. 2020 · 2020
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