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Identifying and Rewarding Subcrowds in Crowdsourcing

Siyuan Liu, Xiuyi Fan, Chunyan Miao

22nd European Conference on Artificial Intelligence, Volume: 285: ECAI 2016, Pages: 1573 - 1574

Swansea University Author: Xiuyi Fan

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DOI (Published version): 10.3233/978-1-61499-672-9-1573

Abstract

Identifying and rewarding truthful workers are key to the sustainability of crowdsourcing platforms. In this paper, we present a clustering based rewarding mechanism that rewards workers based on their truthfulness while accommodating the differences in workers' preferences. Experimental result...

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Published in: 22nd European Conference on Artificial Intelligence
ISBN: 978-1-61499-671-2 978-1-61499-672-9
ISSN: 0922-6389 1879-8314
Published: The Hague, The Netherlands 22nd European Conference on Artificial Intelligence 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa39397
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spelling 2018-04-23T14:05:19.5253498 v2 39397 2018-04-13 Identifying and Rewarding Subcrowds in Crowdsourcing a88a07c43b3e80f27cb96897d1bc2534 Xiuyi Fan Xiuyi Fan true false 2018-04-13 MACS Identifying and rewarding truthful workers are key to the sustainability of crowdsourcing platforms. In this paper, we present a clustering based rewarding mechanism that rewards workers based on their truthfulness while accommodating the differences in workers' preferences. Experimental results show that the proposed approach can effectively discover subcrowds under various conditions, and truthful workers are better rewarded than less truthful ones. Conference Paper/Proceeding/Abstract 22nd European Conference on Artificial Intelligence 285: ECAI 2016 1573 1574 22nd European Conference on Artificial Intelligence The Hague, The Netherlands 978-1-61499-671-2 978-1-61499-672-9 0922-6389 1879-8314 29 8 2016 2016-08-29 10.3233/978-1-61499-672-9-1573 http://ebooks.iospress.nl/volumearticle/44926 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2018-04-23T14:05:19.5253498 2018-04-13T15:19:54.7717106 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Siyuan Liu 1 Xiuyi Fan 2 Chunyan Miao 3 39397__16379__34bb8e1bccce4da9b90a3b2a4bd28c2e.pdf 39397.pdf 2020-01-21T18:16:09.5938123 Output 230454 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution Non-Commercial License 4.0 (CC-BY-NC). true eng https://creativecommons.org/licenses/by-nc/4.0/
title Identifying and Rewarding Subcrowds in Crowdsourcing
spellingShingle Identifying and Rewarding Subcrowds in Crowdsourcing
Xiuyi Fan
title_short Identifying and Rewarding Subcrowds in Crowdsourcing
title_full Identifying and Rewarding Subcrowds in Crowdsourcing
title_fullStr Identifying and Rewarding Subcrowds in Crowdsourcing
title_full_unstemmed Identifying and Rewarding Subcrowds in Crowdsourcing
title_sort Identifying and Rewarding Subcrowds in Crowdsourcing
author_id_str_mv a88a07c43b3e80f27cb96897d1bc2534
author_id_fullname_str_mv a88a07c43b3e80f27cb96897d1bc2534_***_Xiuyi Fan
author Xiuyi Fan
author2 Siyuan Liu
Xiuyi Fan
Chunyan Miao
format Conference Paper/Proceeding/Abstract
container_title 22nd European Conference on Artificial Intelligence
container_volume 285: ECAI 2016
container_start_page 1573
publishDate 2016
institution Swansea University
isbn 978-1-61499-671-2
978-1-61499-672-9
issn 0922-6389
1879-8314
doi_str_mv 10.3233/978-1-61499-672-9-1573
publisher 22nd European Conference on Artificial Intelligence
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://ebooks.iospress.nl/volumearticle/44926
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description Identifying and rewarding truthful workers are key to the sustainability of crowdsourcing platforms. In this paper, we present a clustering based rewarding mechanism that rewards workers based on their truthfulness while accommodating the differences in workers' preferences. Experimental results show that the proposed approach can effectively discover subcrowds under various conditions, and truthful workers are better rewarded than less truthful ones.
published_date 2016-08-29T04:23:54Z
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score 11.047306

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