EndorTrust: An Endorsement-Based Reputation System for Trustworthy and Heterogeneous Crowdsourcing

EndorTrust: An Endorsement-Based Reputation System for Trustworthy and Heterogeneous Crowdsourcing
Title:
EndorTrust: An Endorsement-Based Reputation System for Trustworthy and Heterogeneous Crowdsourcing
Other Titles:
2015 IEEE Global Communications Conference (GLOBECOM)
Keywords:
Publication Date:
06 December 2015
Citation:
C. Wu, T. Luo, F. Wu and G. Chen, "EndorTrust: An Endorsement-Based Reputation System for Trustworthy and Heterogeneous Crowdsourcing," 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, 2015, pp. 1-6. doi: 10.1109/GLOCOM.2015.7417352
Abstract:
Crowdsourcing is a new distributed computing paradigm that leverages the wisdom of crowd and the voluntary human effort to solve problems or collect data. In this context, trustworthiness of user contributions is of crucial importance to the viability of crowdsourcing. Prior mechanisms either do not consider the trustworthiness or quality of contributions or have to assess it only after workers' submission of contributions, which results in irreversible effort expenditure and negative player utilities. In this paper, we propose a reputation system, EndorTrust, to not only assess but also predict the trustworthiness of contributions without wasting workers' effort. The key approach is to explore an inter-worker relationship called endorsement to improve trustworthiness prediction using machine learning methods, while also taking into account the heterogeneity of both workers and tasks.
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PublisherCopyrights
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(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
978-1-4799-5951-8
ISBN:
978-1-4799-5952-5
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