固定成本池策略对测试集合偏差的影响

Aldo Lipani, G. Zuccon, M. Lupu, B. Koopman, A. Hanbury
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引用次数: 18

摘要

在信息检索中,测试集合通常使用池方法构建。针对池化方法,已经开发了许多池化策略。在此,我们解决的问题是,在评估系统时,在需要评估的文件数量上存在预算限制的情况下,使用面向精度的措施来确定最佳池化策略。作为质量测量,我们使用池化策略引入的偏差,根据分数的平均绝对误差和排名误差进行测量。基于15个测试集的实验,我们得出结论,对于面向精度的度量,基于Rank-Biased Precision (RBP)的策略是最佳的。这些结果可以为收集者提供信息,因为它们表明,在固定的评估预算限制下,基于rbp的抽样产生的偏见池比其他选择更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Impact of Fixed-Cost Pooling Strategies on Test Collection Bias
In Information Retrieval, test collections are usually built using the pooling method. Many pooling strategies have been developed for the pooling method. Herein, we address the question of identifying the best pooling strategy when evaluating systems using precision-oriented measures in presence of budget constraints on the number of documents to be evaluated. As a quality measurement we use the bias introduced by the pooling strategy, measured both in terms of Mean Absolute Error of the scores and in terms of ranking errors. Based on experiments on 15 test collections, we conclude that, for precision-oriented measures, the best strategies are based on Rank-Biased Precision (RBP). These results can inform collection builders because they suggest that, under fixed assessment budget constraints, RBP-based sampling produces less biased pools than other alternatives.
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