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Incentives for Item Duplication Under Fair Ranking Policies

Di Nunzio G. M.
•
Fabris A.
•
Silvello G.
•
Susto G. A.
2021
  • conference object

Abstract
Ranking is a fundamental operation in information access systems, to filter information and direct user attention towards items deemed most relevant to them. Due to position bias, items of similar relevance may receive significantly different exposure, raising fairness concerns for item providers and motivating recent research into fair ranking. While the area has progressed dramatically over recent years, no study to date has investigated the potential problem posed by duplicated items. Duplicates and near-duplicates are common in several domains, including marketplaces and document collections available to search engines. In this work, we study the behaviour of different fair ranking policies in the presence of duplicates, quantifying the extra-exposure gained by redundant items. We find that fairness-aware ranking policies may conflict with diversity, due to their potential to incentivize duplication more than policies solely focused on relevance. This fact poses a problem for system owners who, as a result of this incentive, may have to deal with increased redundancy, which is at odds with user satisfaction. Finally, we argue that this aspect represents a blind spot in the normative reasoning underlying common fair ranking metrics, as rewarding providers who duplicate their items with increased exposure seems unfair for the remaining providers.
DOI
10.1007/978-3-030-78818-6_7
Archivio
https://hdl.handle.net/11368/3107445
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85111415356
https://link.springer.com/chapter/10.1007/978-3-030-78818-6_7
Diritti
open access
license:digital rights management non definito
license:copyright editore
license uri:iris.pri00
license uri:iris.pri02
FVG url
https://arts.units.it/request-item?handle=11368/3107445
Soggetti
  • Algorithmic fairne

  • Duplicate

  • Fair ranking

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