A document rating system for preference judgements

Maryam Bashir, J. Anderton, Jie Wu, Peter B. Golbus, Virgil Pavlu, J. Aslam
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引用次数: 19

Abstract

High quality relevance judgments are essential for the evaluation of information retrieval systems. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assessor disagreement and the arbitrariness of grades. Previous research has shown that it is easier for assessors to make pairwise preference judgments. However, unless the preferences collected are largely transitive, it is not clear how to combine them in order to obtain document relevance scores. Another difficulty is that the number of pairs that need to be assessed is quadratic in the number of documents. In this work, we consider the problem of inferring document relevance scores from pairwise preference judgments by analogy to tournaments using the Elo rating system. We show how to combine a linear number of pairwise preference judgments from multiple assessors to compute relevance scores for every document.
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用于偏好判断的文件评级系统
高质量的相关性判断对信息检索系统的评价至关重要。传统的相关性判断收集方法是基于收集二元或分级的名义判断,但这种判断受到评估者之间的分歧和等级的随意性等因素的限制。先前的研究表明,评估者更容易做出两两偏好判断。然而,除非收集到的首选项在很大程度上是可传递的,否则如何将它们结合起来以获得文档相关性分数是不清楚的。另一个困难是需要评估的对的数量是文档数量的二次。在这项工作中,我们考虑了使用Elo评级系统从配对偏好判断中推断文档相关性分数的问题。我们展示了如何结合来自多个评估者的线性数量的两两偏好判断来计算每个文档的相关性分数。
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