Provider fairness across continents in collaborative recommender systems
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Service provider fairness can be managed through post-processing techniques.
“P-fairness can be managed through post-processing techniques [40].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [40]
Cites
Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists
Sapiezynski, P., Zeng, W., E Robertson, R., Mislove, A., & Wilson, C. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317595 · Cited by 92 · see citing works
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Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Gómez, E., Boratto, L., & Salamó, M.
- Affiliations
- Universitat de Barcelona (ES)
- University of Cagliari (IT)
- Corresponding author
- Boratto, Ludovico
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 59
- Issue
- 1
- Pages
- 102719
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W3205185116
- Cited by
- 39
- References
- 1
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 13 · 2023: 6 · 2022: 9
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (acceptedVersion, cc-by-nc-nd)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- recommender systems, fairness, item exposure, indirect discrimination, re-ranking, equity, movie recommendation, book recommendations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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