The Treatment of Ties in Rank-Biased Overlap

TL;DR

This paper introduces a comprehensive approach to handle ties in Rank-Biased Overlap (RBO), enhancing its accuracy and applicability in ranking comparisons by aligning it with statistical methods and demonstrating its effectiveness on real data.

Contribution

It proposes a generalized formulation for tie handling in RBO, completing the original definition and developing variants aligned with statistical rank correlation methods.

Findings

Tie-aware RBO scores differ significantly from original scores.

The new measures provide more consistent and meaningful ranking comparisons.

Synthetic and real data demonstrate the importance of proper tie treatment.

Abstract

Rank-Biased Overlap (RBO) is a similarity measure for indefinite rankings: it is top-weighted, and can be computed when only a prefix of the rankings is known or when they have only some items in common. It is widely used for instance to analyze differences between search engines by comparing the rankings of documents they retrieve for the same queries. In these situations, though, it is very frequent to find tied documents that have the same score. Unfortunately, the treatment of ties in RBO remains superficial and incomplete, in the sense that it is not clear how to calculate it from the ranking prefixes only. In addition, the existing way of dealing with ties is very different from the one traditionally followed in the field of Statistics, most notably found in rank correlation coefficients such as Kendall's and Spearman's. In this paper we propose a generalized formulation for RBO to handle ties, thanks to which we complete the original definitions by showing how to perform prefix evaluation. We also use it to fully develop two variants that align with the ones found in the Statistics literature: one when there is a reference ranking to compare to, and one when there is not. Overall, these three variants provide researchers with flexibility when comparing rankings with RBO, by clearly determining what ties mean, and how they should be treated. Finally, using both synthetic and TREC data, we demonstrate the use of these new tie-aware RBO measures. We show that the scores may differ substantially from the original tie-unaware RBO measure, where ties had to be broken at random or by arbitrary criteria such as by document ID. Overall, these results evidence the need for a proper account of ties in rank similarity measures such as RBO.