Fetching the paper…
Reading the bibliography…
Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a robust choice for optimizing relevance and fairness metrics.
Statistical theory of extreme values and some practical applications: a series of lectures . Vol. 33
Emil Julius Gumbel. 1954 · 1954
Earlier work this paper cites.
The analysis of permutations
Robin L Plackett. 1975 · 1975
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Optimizing Search Engines Using Clickthrough Data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 133–142
Thorsten Joachims. 2002 · 2002
Earlier work this paper cites.
Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning . 89–96
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning . 129–136
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
An experimental comparison of click position-bias models. In Proceedings of the 2008 international conference on web search and data mining . 87–94
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008 · 2008
Earlier work this paper cites.
Softrank: optimizing non-smooth rank metrics. In Proceedings of the 2008 International Conference on Web Search and Data Mining . 77–86
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008 · 2008
Earlier work this paper cites.
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
Earlier work this paper cites.
On the local optimality of LambdaRank. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval . 460–467
Pinar Donmez, Krysta M Svore, and Christopher JC Burges. 2009 · 2009
Earlier work this paper cites.
Learning to Rank for Information Retrieval
Tie-Yan Liu. 2009 · 2009
Earlier work this paper cites.
From RankNet to LambdaRank to LambdaMART: An Overview
Christopher J.C. Burges. 2010 · 2010
Cited alongside, same era.
Yahoo! Learning to Rank Challenge Overview
Olivier Chapelle and Yi Chang. 2011 · 2011
Cited alongside, same era.
A Probabilistic Method for Inferring Preferences from Clicks. In CIKM . ACM, 249–258
Katja Hofmann, Shimon Whiteson, and Maarten de Rijke. 2011 · 2011
Cited alongside, same era.
Individual choice behavior: A theoretical analysis
R Duncan Luce. 2012 · 2012
Cited alongside, same era.
Introducing LETOR 4.0 datasets
Tao Qin and Tie-Yan Liu. 2013 · 2013
Cited alongside, same era.
Probabilistic multileave for online retrieval evaluation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval . 955–958
Differentiable Unbiased Online Learning to Rank. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . ACM, 1293–1302
Harrie Oosterhuis and Maarten de Rijke. 2018 · 2018
Later among the works it cites.
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 . 1241–1244
Sebastian Bruch, Masrour Zoghi, Michael Bendersky, and Marc Najork. 2019 · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems . 8026–8037
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems . 5426–5436
Ashudeep Singh and Thorsten Joachims. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Anne Schuth, Robert-Jan Bruintjes, Fritjof Buüttner, Joost van Doorn, Carla Groenland, Harrie Oosterhuis, Cong-Nguyen Tran, Bas Veeling, Jos van der Velde, Roger Wechsler, et al · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation OSDI’16) . 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees
Domenico Dato, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, and Rossano Venturini. 2016 · 2016
Cited alongside, same era.
Reinforcement learning to rank with Markov decision process. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 945–948
Zeng Wei, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
Adapting Markov decision process for search result diversification. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 535–544
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
Equity of attention: Amortizing individual fairness in rankings. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 405–414
Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018 · 2018
Cited alongside, same era.
Position Bias Estimation for Unbiased Learning to Rank in Personal Search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 610–618
Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. 2018a
Cited in the paper.
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
Later among the works it cites.
Evaluating Stochastic Rankings with Expected Exposure
Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020 · 2020
Later among the works it cites.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, St’efan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fern’andez del R’ıo, Mark Wiebe, Pearu Peterson, Pierre G’erard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
Later among the works it cites.
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020 · 2020
Later among the works it cites.
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. In Proceedings of the 2020 International Conference on The Theory of Information Retrieval . ACM
Harrie Oosterhuis and Maarten de Rijke. 2020 · 2020
Later among the works it cites.
Towards Meaningful Statements in IR Evaluation. Mapping Evaluation Measures to Interval Scales
Marco Ferrante, Nicola Ferro, and Norbert Fuhr. 2021 · 2021
Closest in time.
Unifying Online and Counterfactual Learning to Rank. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM’21) . ACM
Harrie Oosterhuis and Maarten de Rijke. 2021 · 2021
Closest in time.