RankLib is a library of learning to rank algorithms. Currently eight popular algorithms have been implemented:

• MART (Multiple Additive Regression Trees, a.k.a. Gradient boosted regression tree) [6]
• RankNet [1]
• RankBoost [2]
• AdaRank [3]
• Coordinate Ascent [4]
• LambdaMART [5]
• ListNet [7]
• Random Forests [8]

It also implements many retrieval metrics as well as provides many ways to carry out evaluation.

This project forked from The Lemur Project.

Versions in Maven Repository

RankLib is available under BSD license.

1. C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender. Learning to rank using gradient descent. In Proc. of ICML, pages 89-96, 2005.
2. Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 4: 933-969, 2003.
3. J. Xu and H. Li. AdaRank: a boosting algorithm for information retrieval. In Proc. of SIGIR, pages 391-398, 2007.
4. D. Metzler and W.B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3): 257-274, 2007.
5. Q. Wu, C.J.C. Burges, K. Svore and J. Gao. Adapting Boosting for Information Retrieval Measures. Journal of Information Retrieval, 2007.
6. J.H. Friedman. Greedy function approximation: A gradient boosting machine. Technical Report, IMS Reitz Lecture, Stanford, 1999; see also Annals of Statistics, 2001.
7. Z. Cao, T. Qin, T.Y. Liu, M. Tsai and H. Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
8. L. Breiman. Random Forests. Machine Learning 45 (1): 5–32, 2001.