A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings

We describe rbstar a toolkit of software for carrying out measurements when the goal is to determine how similar a system observation is to a gold-standard reference output. The resource covers all four combinations that arise when each of observation and reference can be either an unordered finite set in which element ordering is unimportant, or a finite prefix of an arbitrarily long ranking in which early elements are more important than later ones. Specifically, the package realizes four ''rank-biased'' measurement approaches that have been presented in a sequence of papers over a 15-year span, bringing them together into a single location with a uniform interface and efficient reference implementations. The provision of all of rank-biased precision, rank-biased overlap, rank-biased recall, and rank-biased alignment, with the latter two recent additions to the family, allows a wide range of measurement scenarios to be handled in a consistent manner.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730306
Citations
1
Primary Topic
Information Retrieval and Search Behavior
Type
article
Field-Weighted Citation Impact
3.25
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article

A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings

Antonio Mallia, Joel Mackenzie, Alistair Moffat, Matthias Petri
1 citations
Information Retrieval and Search Behavior
3.25
article

A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings

Antonio Mallia, Joel Mackenzie, Alistair Moffat, Matthias Petri
article en
1 citations

Abstract

We describe rbstar a toolkit of software for carrying out measurements when the goal is to determine how similar a system observation is to a gold-standard reference output. The resource covers all four combinations that arise when each of observation and reference can be either an unordered finite set in which element ordering is unimportant, or a finite prefix of an arbitrarily long ranking in which early elements are more important than later ones. Specifically, the package realizes four ''rank-biased'' measurement approaches that have been presented in a sequence of papers over a 15-year span, bringing them together into a single location with a uniform interface and efficient reference implementations. The provision of all of rank-biased precision, rank-biased overlap, rank-biased recall, and rank-biased alignment, with the latter two recent additions to the family, allows a wide range of measurement scenarios to be handled in a consistent manner.

Amazon (United States) (US), The University of Queensland (AU), The University of Melbourne (AU), PricewaterhouseCoopers (United States) (US)
Openalex Percentile: Top 8%
Information Retrieval and Search Behavior
3.25
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A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings — Antonio Mallia, Joel Mackenzie, et al. · (2025) | TGRS Research Map | TGRS