mlsanityData to 14 Oct 2026

Proportional Rankings

arXiv 1612.01434 ↗First posted 5 Dec 2016cs.GT cs.AI cs.MAcited by 10

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Abstract (arXiv)

In this paper we extend the principle of proportional representation to rankings. We consider the setting where alternatives need to be ranked based on approval preferences. In this setting, proportional representation requires that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Proportional rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., hiring decisions (if it is unclear how many positions are to be filled), the presentation of competing proposals on a liquid democracy platform (if it is unclear how many proposals participants are taking into consideration), or recommender systems (if a ranking has to accommodate different user types). We study the proportional representation provided by several ranking methods and prove theoretical guarantees. Furthermore, we experimentally evaluate these methods and present preliminary evidence as to which methods are most suitable for producing proportional rankings.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2016.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 3; 2026: 3.

10 papers.