In this demo paper we propose RankEval, an open-source tool for the analysis and evaluation of Learning-To-Rank (LtR) models based on ensembles of regression trees. Gradient Boosted Regression Trees (GBRT) is a flexible statistical learning technique for classifi-cation and regression at the state of the art for training effective LtR solutions. Indeed, the success of GBRT fostered the development of several open-source LtR libraries targeting efficiency of the learning phase and effectiveness of the resulting models. However, these libraries offer only very limited help for the tuning and evaluation of the trained models. In addition, the implementations provided for even the most traditional IR evaluation metrics differ from library to library, thus making the objective evaluation and comparison between trained models a difficult task. RankEval addresses these issues by providing a common ground for LtR libraries that offers useful and interoperable tools for a comprehensive comparison and in-depth analysis of ranking models.
|Data di pubblicazione:||2017|
|Titolo:||RankEval: An evaluation and analysis framework for learning-To-rank solutions|
|Titolo del libro:||SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1145/3077136.3084140|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|