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On Theoretically Valid Score Distributions in Information Retrieval

机译:信息检索理论上的有效分数分布

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In this paper, we aim to investigate the practical usefulness of the Recall-Fallout Convexity Hypothesis (RFCH) for a number of document score distribution (SD) models. We compare SD models that do not automatically adhere to the RFCH to modified versions of the same SD models that do adhere to the RFCH. We compare these models using the inference of average precision as a measure of utility. For the three models studied in this paper, we conclude that adhering to the RFCH is practically useful for the two-normal model, makes no difference for the two-gamma model, and degrades the performance of the two-lognormal model.
机译:在本文中,我们旨在研究召回落差凸性假设(RFCH)在许多文档分数分布(SD)模型中的实用性。我们将不自动遵循RFCH的SD模型与确实遵循RFCH的相同SD模型的修改版本进行了比较。我们使用平均精度推断作为效用的度量来比较这些模型。对于本文研究的三个模型,我们得出结论,坚持RFCH对于两个正态模型实际上是有用的,对于两个γ模型没有区别,并且会降低两个对数正态模型的性能。

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