research-article

Client Clustering for Hiring Modeling in Work Marketplaces

Authors:

Vasilis Verroios,

Panagiotis Papadimitriou,

Hector Garcia-MolinaAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2187 - 2196

https://doi.org/10.1145/2783258.2788589

Published: 10 August 2015 Publication History

Abstract

An important problem that online work marketplaces face is grouping clients into clusters, so that in each cluster clients are similar with respect to their hiring criteria. Such a separation allows the marketplace to "learn" more accurately the hiring criteria in each cluster and recommend the right contractor to each client, for a successful collaboration. We propose a Maximum Likelihood definition of the "optimal" client clustering along with an efficient Expectation-Maximization clustering algorithm that can be applied in large marketplaces. Our results on the job hirings at oDesk over a seven-month period show that our client-clustering approach yields significant gains compared to "learning" the same hiring criteria for all clients. In addition, we analyze the clustering results to find interesting differences between the hiring criteria in the different groups of clients.

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Cited By

Disser YFearnley JGairing MGöbel OKlimm MSchmand DSkopalik ATönnis A(2020)Hiring Secretaries over TimeMathematics of Operations Research10.1287/moor.2019.099345:1(323-352)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1287/moor.2019.0993
Mezzanzanica MMercorio F(2019)Big Data Enables Labor Market IntelligenceEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_276(226-236)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_276
Mezzanzanica MMercorio F(2018)Big Data Enables Labor Market IntelligenceEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_276-1(1-11)Online publication date: 1-Feb-2018
https://doi.org/10.1007/978-3-319-63962-8_276-1
Show More Cited By

Index Terms

Client Clustering for Hiring Modeling in Work Marketplaces
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 August 2015

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Disser YFearnley JGairing MGöbel OKlimm MSchmand DSkopalik ATönnis A(2020)Hiring Secretaries over TimeMathematics of Operations Research10.1287/moor.2019.099345:1(323-352)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1287/moor.2019.0993
Mezzanzanica MMercorio F(2019)Big Data Enables Labor Market IntelligenceEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_276(226-236)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_276
Mezzanzanica MMercorio F(2018)Big Data Enables Labor Market IntelligenceEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_276-1(1-11)Online publication date: 1-Feb-2018
https://doi.org/10.1007/978-3-319-63962-8_276-1
Wolfengagen VIsmailova LKosikov S(2016)A harmony and disharmony in mining of the migrating individuals2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)10.1109/DIPDMWC.2016.7529363(52-57)Online publication date: Jul-2016
https://doi.org/10.1109/DIPDMWC.2016.7529363
Wolfengagen VIsmailova LKosikov SParfenova IErmak MPetrov VNikulin IKholodov V(2016)Migration of the IndividualsProcedia Computer Science10.1016/j.procs.2016.07.44988(359-364)Online publication date: 2016
https://doi.org/10.1016/j.procs.2016.07.449

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