About QuickRank

QuickRank is available under Reciprocal Public License 1.5 license.

You can get the source code of QuickRank by cloning the QuickRank GitHub repository. The GitHub repository provides installation instructions and a user guide.

We welcome comments and contribution!

Learning-to-Rank Algorithms

QuickRank is an efficient Learning to Rank toolkit providing multithreaded C++ implementation of several 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.
  • LineSearch: D. G. Luenberger. Linear and nonlinear programming. Addison Wesley, 1984.
  • 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).

QuickRank introduces also the concept of pre and post learning optimizations which are pipelined with the LtR algorithms. Currently implemented optimizers are:

  • CLEAVER: C. Lucchese, F. M. Nardini, S. Orlando, R. Perego, F. Silvestri, S. Trani. Post-Learning Optimization of Tree Ensembles for Efficient Ranking. ACM SIGIR, 2016.

Acknowledgements

If you use QuickRank, please acknowledge the following paper:

  • Capannini, G., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. Quality versus efficiency in document scoring with learning-to-rank models. Information Processing & Management (2016). LINK.

If you use the CLEAVER, please acknowledge the following paper:

  • C. Lucchese, F. M. Nardini, S. Orlando, R. Perego, F. Silvestri, S. Trani. Post-Learning Optimization of Tree Ensembles for Efficient Ranking. ACM SIGIR Conference on Research and Development in Information Retrieval, (2016). LINK.

We will be happy to know that you are using QuickRank and to acknowledge you on the QuickRank GitHub page.