QuickRank is an efficient Learning to Rank toolkit providing several C++ implementation of LtR algorithms. QuickRank was designed and developed with efficiency in mind.
The algorithms currently implemented are:
- GBRT: J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, pages 1189–1232, 2001.
- LambdaMART: Q. Wu, C. Burges, K. Svore, and J. Gao. Adapting boosting for information retrieval measures. Information Retrieval, 2010.
- Oblivious GBRT / LambdaMART: Inspired to I. Segalovich. Machine learning in search quality at yandex. Invited Talk, SIGIR, 2010.
- CoordinateAscent: Metzler, D., Croft, W.B.. Linear feature-based models for information retrieval. Information Retrieval 10(3), pages 257–274, 2007.
- RankBoost: Freund, Y., Iyer, R., Schapire, R. E., & Singer, Y. An efficient boosting algorithm for combining preferences. The Journal of machine learning research, 4, 933-969 (2003).
Check out further information and code documentation.
QuickRank is presented in:
- Gabriele Capannini, Domenico Dato, Claudio Lucchese, Monica Mori, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto. QuickRank: a C++ Suite of Learning to Rank Algorithms. Proceedings of the 6th Italian Information Retrieval Workshop (IIR 2015). Cagliari (Italy). LINK
QuickRank has been used in:
- C. Lucchese, F. M. Nardini, S. Orlando, R. Perego, N. Tonellotto, R. Venturini. QuickScorer: a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees. Full Paper. SIGIR 2015: Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 2015. BEST PAPER AWARD. LINK
© Contributors, 2016. Licensed under an Reciprocal Public License (RPL-1.5).