Abstract—Crowdsourcing is a promising
platform, whereby massive tasks are broadcasted to a crowd of semi-skilled
workers by the requester for reliable solutions. In this paper, we consider
four key evaluation indices of a crowdsourcing community (i.e. quality, cost,
latency, and platform improvement), and demonstrate that these indices involve
the interests of the three stakeholders, namely requester, worker and
crowdsourcing platform. Since the incentives among these three stakeholders
always conflict with each other, to elevate the long-term development of the
crowdsourcing community, we take the perspective of the whole crowdsourcing
community, and design a crowdsourcing mechanism to align incentives of
stakeholders together. Specifically, we give workers reward or penalty according
to their reporting solutions instead of only nonnegative payment. Furthermore,
we find a series of proper reward-penalty function pairs and compute workers
personal order values, which can provide different amounts of reward and
penalty according to both the workers reporting beliefs and their individual
history performances, and keep the incentive of workers at the same time. The
proposed mechanism can help latency control, promote quality and platform
evolution of crowdsourcing community, and improve the aforementioned four key
evaluation indices. Theoretical analysis and experimental results are provided
to validate and evaluate the proposed mechanism respectively.
CONCLUSION
In this paper, we have demonstrated that a crowdsourcing
community involves the interests of the three stakeholders, namely requester,
worker and crowdsourcing platform, and the incentives among them always
conflict with each other. We have proposed and verified the hypothesis that all
workers believe that in most cases they observe the real solution of each task
perturbed only by unbiased noise, and Any Query Call Us:
9566355386
design a crowdsourcing
mechanism, encompassing a series of proper reward-penalty function pairs and
workers’ personal order values, to align the interests of different
stakeholders, which has been validated by the theoretical analysis and
experimental results. This work can help to relieve the platform and requesters
of crowdsourcing community from monitoring workers’ efforts and capacities in
performing crowdsourcing tasks, save the costs of requesters, and attract more
professional workers to the crowdsourcing platforms. It can accelerate the
long-term development of the whole crowdsourcing community.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
• System : Pentium IV
2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44
Mb.
• Monitor : 15 VGA
Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
• Operating system : -
Windows XP/7.
• Coding Language :
JAVA/J2EE
• Data Base : MYSQL
REFERENCES Any Query Call Us:
9566355386
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Conference on Machine Learning Workshop (ICML), pp. 1–19, MIT Press, 2013.
[3] Y. Gao, Y. Chen, and K. J. R. Liu, ―On
cost-effective incentive mechanisms in microtask crowdsourcing,‖ IEEE
Transactions on Computational Intelligence and AI in Games, vol. 7, pp. 3–15, 3
2015.