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  Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform

Sühr, T., Biega, A. J., Zehlike, M., Gummadi, K., & Chakraborty, A. (2019). Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform. In KDD'19 (pp. 3082-3092). New York, NY: ACM. doi:10.1145/3292500.3330793.

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Genre: Conference Paper
Latex : Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: {A} Case Study of a Ride-Hailing Platform

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two_sided_fairness_for_matchings_kdd2019.pdf (Preprint), 968KB
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two_sided_fairness_for_matchings_kdd2019.pdf
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© Owner/Author 2019. blication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, http://dx.doi.org/10.1145/3292500.3330793
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 Creators:
Sühr, Tom1, Author           
Biega, Asia J.2, Author           
Zehlike, Meike1, Author           
Gummadi, Krishna1, Author           
Chakraborty, Abhijnan1, Author           
Affiliations:
1Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society, ou_2105291              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 20192019
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Suehr_KDD2019
DOI: 10.1145/3292500.3330793
 Degree: -

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Title: 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Place of Event: Anchorage, AK, USA
Start-/End Date: 2019-08-04 - 2019-08-08

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Project name : FairSocialComputing
Grant ID : 789373
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: KDD'19
  Subtitle : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  Other : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  Abbreviation : KDD 2019
Source Genre: Proceedings
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Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3082 - 3092 Identifier: ISBN: 978-1-4503-6201-6