Research Products

RankEval is an open-source tool for the analysis and evaluation of Learning-to-Rank models based on ensembles of regression trees. The success of ensembles of regression trees fostered the development of several open-source libraries targeting efficiency of the learning phase and effectiveness of the resulting models. RankEval aims at providing a common ground for several Learning to Rank libraries by providing useful and interoperable tools for a comprehensive comparison and in-depth analysis of ranking models. Clone and try it from GitHub!

QuickRank is an efficient Learning to Rank toolkit providing several C++ implementation of LtR algorithms. The algorithms currently implemented are: GBRT, LambdaMART, Oblivious GBRT and LambdaMART, CoordinateAscent, RankBoost. It has been designed to be efficient. It is available under Reciprocal Public License 1.5 license. Clone and try it from GitHub!