NDLI: Crowd Trust: A Context-Aware Trust Model for Worker Selection in Crowdsourcing Environments

Content Provider	IEEE Xplore Digital Library
Author	Bin Ye Yan Wang Ling Liu
Copyright Year	2015
Description	Author affiliation: Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA (Ling Liu) \|\| Dept. of Comput., Macquarie Univ., Sydney, NSW, Australia (Bin Ye; Yan Wang)
Abstract	On a crowd sourcing platform consisting of task publishers and workers, it is critical for a task publisher to select trustworthy workers to solve human intelligence tasks (HITs). Currently, the prevalent trust evaluation mechanism employs the overall approval rate of HITs, with which dishonest workers can easily succeed in pursuing the maximal profit by quickly giving plausible answers or counterfeiting HITs approval rates. In crowd sourcing environments, a worker's trustworthiness varies in contexts, i.e. It varies in different types of tasks and different reward amounts of tasks. Thus, we propose two classifications based on task types and task reward amount respectively. On the basis of the classifications, we propose a trust evaluation model, which consists of two types of context-aware trust: task type based trust (TaTrust) and reward amount based trust (RaTrust). Then, we model trustworthy worker selection as a multi-objective combinatorial optimization problem, which is NP-hard. For solving this challenging problem, we propose an evolutionary algorithm MOWS_GA based on NSGA-II. The results of experiments illustrate that our proposed trust evaluation model can effectively differentiate honest workers and dishonest workers when both of them have high overall HITs approval rates.
Starting Page	121
Ending Page	128
File Size	266742
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Crowdsourcing Context modeling Context Optimization Evolutionary computation Computational modeling Australia Combinatorial Optimization Contextual Trust Worker Selection
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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