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L2RLab: Integrated experimenter environment for learning to rank

Abstract:

L2RLab is a development environment that lets us to integrate all the stages to develop, evaluate, compare and analyze the performance of new learning-to-rank models. It contains tools for individual and multiple pre-processed of the data collections, it also lets us to study the influence of the features in the ranking, the format conversion (e.g., Weka's.ARFF) and visualization. This software facilitates the comparison between two or more methods taking as parameters the performance achieved in the ranking, also includes functionalities for the statistical analysis on the query-level precision of the algorithm proposed regarding to those referenced in the literature. The study of the learning curves' behavior of the different methods is another feature of the tool. L2RLab is programmed in java and is designed as a tool oriented to the extensibility, therefore, the addition of new functionalities is an easy task. L2RLab has an easy-to-use interface that avoids the reprogramming of the applications for our experiments. Basically, L2RLab is structured by two main modules: the visual application and a framework that facilitates the inclusion of the new algorithms and the performance measures developed by the researcher. © 2013 Springer-Verlag Berlin Heidelberg.