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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Joachims, T.: A support vector method for multivariate performance measures. In: ICML 22, pp. 377–384 (2005)
Valizadegan, H., Jin, R., Zhang, R., Mao, J.: Learning to Rank by Optimizing NDCG Measure. In: Proceedings of NIPS 23, pp. 1883–1891 (2009)
Wu, J., Yang, Z., Lin, Y., Lin, H., Ye, Z., Xu, K.: Learning to Rank Using Query-Level Regression. In: SIGIR 2011: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, pp. 1091–1092 (2011)
Xu, J., Li, H.: Adarank: a boosting algorithm for information retrieval. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, Netherlands, pp. 391–398 (2007)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., p. 560. Morgan Kaufmann Publishers, San Francisco (2005)
Fayad, M.E., Schmidt, D.C., Johnson, R.E.: Building application frameworks: Object oriented foundations of framework design. Application Frameworks, ch., 1st edn., p. 638 (1999)
Liu, T.-Y., Xu, J., Qin, T., Xiong, W., Li, H.: Letor: Benchmark dataset for research on learning to rank for information retrieval. In: Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (2007)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002)
Cárdenas, J.: Análisis Univariado. Capítulo # 6.- Análisis de varianza multifactorial. In: C.-A.d.v.m.D.J.R.C.P.F.d.E (eds.) UCLV, Facultad de Economía, UCLV (2003)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics 11, 86–92 (1940)
Nemenyi, P.B.: Distribution-free multiple comparisons. PhD thesis, Princeton University (1963)
Alejo, O.J., Fernández, J.M., Huete, J.F.: RankPSO: a new L2R algorithm based on Particle Swarm Optimization. Waitin for being published in Journal of Multiple-Valued Logic and Soft Computing (JMVLSC) (2013)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions Evolutionary Computation 6(1), 58–73 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alejo, Ó.J., Fernández-Luna, J.M., Huete, J.F., Moreno-Cerrud, E. (2013). L2RLab: Integrated Experimenter Environment for Learning to Rank. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-40769-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40768-0
Online ISBN: 978-3-642-40769-7
eBook Packages: Computer ScienceComputer Science (R0)