HPC Lab GitHub page: https://github.com/hpclab
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 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 is available under Reciprocal Public License 1.5 license. Clone it from GitHub!
TripBuilder, is an user-friendly and interactive system for planning a time-budgeted sightseeing tour of a city on the basis of the points of interest and the patterns of movements of tourists mined from user-contributed data. The knowledge needed to build the recommendation model is entirely extracted in an unsupervised way from two popular collaborative platforms: Wikipedia and Flickr. TripBuilder interacts with the user by means of a friendly Web interface that allows her to easily specify personal interests and time budget. The sightseeing tour proposed can be then explored and modified. TripBuilder demo won the best demo award at ECIR 2014. Plan your visit to Rome, Florence, Pisa and Amsterdam by clicking here .
SearchShortcuts, is an efficient and effective solution to the problem of choosing the queries to suggest to web search engine users in order to help them in rapidly satisfying their information needs. SearchShortcuts is less affected by the data-sparsity problem than most state-of-the-art proposals. Thus, it is particularly effective in generating suggestions for rare queries occurring in the long tail of the query popularity distribution.