Name
Title | ||
|---|---|---|
A capability requirements approach for predicting worker performance in crowdsourcing |
Abstract | ||
|---|---|---|
Assigning heterogeneous tasks to workers is an important challenge of crowdsourcing platforms. Current approaches to task assignment have primarily focused on content-based approaches, qualifications, or work history. We propose an alternative and complementary approach that focuses on what capabilities workers employ to perform tasks. First, we model various tasks according to the human capabilities required to perform them. Second, we capture the capability traces of the crowd workers performance on existing tasks. Third, we predict performance of workers on new tasks to make task routing decisions, with the help of capability traces. We evaluate the effectiveness of our approach on three different tasks including fact verification, image comparison, and information extraction. The results demonstrate that we can predict worker's performance based on worker capabilities. We also highlight limitations and extensions of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.4108/icst.collaboratecom.2013.254181 | Collaborative Computing: Networking, Applications and Worksharing |
Keywords | Field | DocType |
mobile computing,capability requirements approach,content-based approaches,crowd workers performance,crowdsourcing,crowdsourcing platforms,fact verification,image comparison,information extraction,task assignment,task routing decisions,work history,worker performance prediction,crowdsourcing,microtask,performance,taxonomy | Data science,Mobile computing,Data mining,Crowdsourcing,Work history,Computer science,Information extraction,Distributed computing | Conference |
Citations | PageRank | References |
6 | 0.55 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umair ul Hassan | 1 | 52 | 7.54 |
| Edward Curry | 2 | 300 | 31.46 |