The University of Southampton
University of Southampton Institutional Repository

A workload-dependent task assignment policy for crowdsourcing

A workload-dependent task assignment policy for crowdsourcing
A workload-dependent task assignment policy for crowdsourcing
Crowdsourcing marketplaces have emerged as an effective tool for high-speed, low-cost labeling of massive data sets. Since the labeling accuracy can greatly vary from worker to worker, we are faced with the problem of assigning labeling tasks to workers so as to maximize the accuracy associated with their answers. In this work, we study the problem of assigning workers to tasks under the assumption that workers' reliability could change depending on their workload, as a result of, e.g., fatigue and learning. We offer empirical evidence of the existence of a workload-dependent accuracy variation among workers, and propose solution procedures for our Crowdsourced Labeling Task Assignment Problem, which we validate on both synthetic and real data sets.
1573-1413
Catallo, Ilio
7cb40ca3-83a7-4e25-aaf4-449dc5c3c358
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Fraternali, Piero
69d3e822-3b59-4fb5-8256-ef827a488dd1
Martinenghi, Davide
e7cf75bb-7f65-4758-ace0-d5925e3181c1
Catallo, Ilio
7cb40ca3-83a7-4e25-aaf4-449dc5c3c358
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Fraternali, Piero
69d3e822-3b59-4fb5-8256-ef827a488dd1
Martinenghi, Davide
e7cf75bb-7f65-4758-ace0-d5925e3181c1

Catallo, Ilio, Coniglio, Stefano, Fraternali, Piero and Martinenghi, Davide (2017) A workload-dependent task assignment policy for crowdsourcing. World Wide Web. (doi:10.1007/s11280-016-0428-7).

Record type: Article

Abstract

Crowdsourcing marketplaces have emerged as an effective tool for high-speed, low-cost labeling of massive data sets. Since the labeling accuracy can greatly vary from worker to worker, we are faced with the problem of assigning labeling tasks to workers so as to maximize the accuracy associated with their answers. In this work, we study the problem of assigning workers to tasks under the assumption that workers' reliability could change depending on their workload, as a result of, e.g., fatigue and learning. We offer empirical evidence of the existence of a workload-dependent accuracy variation among workers, and propose solution procedures for our Crowdsourced Labeling Task Assignment Problem, which we validate on both synthetic and real data sets.

Text
task-assignment - Accepted Manuscript
Download (844kB)

More information

Accepted/In Press date: 12 December 2016
e-pub ahead of print date: 14 January 2017
Published date: 14 January 2017
Organisations: Operational Research

Identifiers

Local EPrints ID: 406193
URI: https://eprints.soton.ac.uk/id/eprint/406193
ISSN: 1573-1413
PURE UUID: 79e9b2f0-0938-46c3-bd04-3f178682a5a8
ORCID for Stefano Coniglio: ORCID iD orcid.org/0000-0001-9568-4385

Catalogue record

Date deposited: 10 Mar 2017 10:41
Last modified: 24 May 2019 00:29

Export record

Altmetrics

Contributors

Author: Ilio Catallo
Author: Piero Fraternali
Author: Davide Martinenghi

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×