RankLib is a library of learning to rank algorithms. Currently eight popular algorithms have been implemented:
It also implements many retrieval metrics as well as provides many ways to carry out evaluation.
Use the sourceforge toolbar right above this page: "Tickets" -> "Bugs" and "Feature Requests".
Use the discussion forum.
If you want to contribute (ideas, codes, etc.) to make RankLib better, let me know in the community contribution forum.
Older versions of RankLib (before it moved into the Lemur Project) can be found here.
RankLib is available under BSD license.
 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.
 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.
 J. Xu and H. Li. AdaRank: a boosting algorithm for information retrieval. In Proc. of SIGIR, pages 391-398, 2007.
 D. Metzler and W.B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3): 257-274, 2007.
 Q. Wu, C.J.C. Burges, K. Svore and J. Gao. Adapting Boosting for Information Retrieval Measures. Journal of Information Retrieval, 2007.
 J.H. Friedman. Greedy function approximation: A gradient boosting machine. Technical Report, IMS Reitz Lecture, Stanford, 1999; see also Annals of Statistics, 2001.
 Z. Cao, T. Qin, T.Y. Liu, M. Tsai and H. Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
 L. Breiman. Random Forests. Machine Learning 45 (1): 5–32, 2001.