789 research outputs found
The Importance of Being Earnest in Crowdsourcing Systems
This paper presents the first systematic investigation of the potential
performance gains for crowdsourcing systems, deriving from available
information at the requester about individual worker earnestness (reputation).
In particular, we first formalize the optimal task assignment problem when
workers' reputation estimates are available, as the maximization of a monotone
(submodular) function subject to Matroid constraints. Then, being the optimal
problem NP-hard, we propose a simple but efficient greedy heuristic task
allocation algorithm. We also propose a simple ``maximum a-posteriori``
decision rule. Finally, we test and compare different solutions, showing that
system performance can greatly benefit from information about workers'
reputation. Our main findings are that: i) even largely inaccurate estimates of
workers' reputation can be effectively exploited in the task assignment to
greatly improve system performance; ii) the performance of the maximum
a-posteriori decision rule quickly degrades as worker reputation estimates
become inaccurate; iii) when workers' reputation estimates are significantly
inaccurate, the best performance can be obtained by combining our proposed task
assignment algorithm with the LRA decision rule introduced in the literature.Comment: To appear at Infocom 201
Mapping for the Masses: Accessing Web 2.0 through Crowdsourcing
The authors describe how we are harnessing the power of web 2.0 technologies to create new approaches to collecting, mapping, and sharing geocoded data. The authors begin with GMapCreator that lets users fashion new maps using Google Maps as a base. The authors then describe MapTube that enables users to archive maps and demonstrate how it can be used in a variety of contexts to share map information, to put existing maps into a form that can be shared, and to create new maps from the bottom-up using a combination of crowdcasting, crowdsourcing, and traditional broadcasting. The authors conclude by arguing that such tools are helping to define a neogeography that is essentially "mapping for the masses,'' while noting that there are many issues of quality, accuracy, copyright, and trust that will influence the impact of these tools on map-based communication
How ECS Improve Creative Use of Employeesâ Knowledge?
Recently, organizations are using crowdsourcing systems (CSs) to collect innovative ideas from their employees harnessing their insights of companiesâ products, processes, customers, and competitors. While crowd workers in third-party CSs are a diverse and multifaceted population with a range of motives and experience, and yet few researchers have grappled with the facilitators of the employeesâ behavior comprising the creative application of their knowledge using enterprise CSs. This study develops a theoretical framework to identify enterprise CSs role and to provide the way how CSs are related to creative behavior via knowledge sharing. In this research, we used a survey to collect data from organizational employees and conducted data analysis to understand how enterprise CSs affect employeesâ creative knowledge application. The findings of this study can help organization refine their ECSs and innovative initiatives
The Importance of Worker Reputation Information in Microtask-Based Crowd Work Systems
This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving
from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workersâ reputation estimates are available, as the maximization of a monotone (sub-modular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple âmaximum a-posterioriâ decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workersâ reputation. Our main findings are that: i) even largely inaccurate estimates of workersâ reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workersâ reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm
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