| A Support Vector Method for Optimizing Average Precision Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims |
| BagBoo: A Scalable Hybrid Bagging-the-Boosting Model Dmitry Pavlov, Alexey Gorodilov, Cliff Brun |
| CS646: Information Retrieval Lecture 16: Learning-to-rank Jiepu Jiang |
| Dynamic Bayesian Network Click Model for Web Search Ranking Olivier Chapelle, Ya Zhang |
| Early Exit Optimizations for Additive Machine Learned Ranking Systems Barla Cambazoglu, Hugo Zaragoza, Olivier Chapelle, Jiang Chen, Ciya Liao, Zhaohui Zheng |
| Efficient Algorithms for Ranking with SVMs O. Chapelle, S. S. Keerthi |
| Future directions in learning to rank Olivier Chapelle, Yi Chang, Tie-Yan Liu |
| Large Scale Learning to Rank D. Sculley |
| Learning in a Pairwise Term-Term Proximity Framework for Information Retrieval Ronan Cummins, Colm O’Riordan |
| Learning to rank Andrew Trotman |
| Learning to Rank (ACML 2009 tutorial) Hang Li |
| Learning to Rank for Information Retrieval (WWW2009 tutorial) - Learning to rank tutorial
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| Learning to Rank using Gradient Descent Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Greg Hullende |
| Learning to rank: from pairwise approach to listwise approach Z Cao, T Qin, TY Liu, MF Tsai, Hang Li |
| Lerot: an Online Learning to Rank Framework. A. Schuth, K. Hofmann, S. Whiteson, M. de Rijke |
| Optimizing Search Engines Using Clickthrough Data Thorsten Joachims |
| SVM Tutorial: Classification, Regression, and Ranking Hwanjo Yu, Sungchul Kim |
| Using gradient descent to optimize language modeling smoothing parameters Donald Metzler |
| Web-Search Ranking with Initialized Gradient Boosted Regression Trees Ananth Mohan, Zheng Chen, Kilian Weinberge |
| Winning The Transfer Learning Track of Yahoo! Andrey Gulin, Igor Kuralenok, Dmitry Pavlov |
| Yahoo! Learning to Rank Challenge Overview Olivier Chapelle, Yi Chan |
