Paper Info
Title
Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems
Abstract
Crowdsourcing market systems (CMS) are platforms that enable one to publish tasks that others are intended to accomplished. Usually, these are systems where users, called workers, perform tasks using desktop computers. Recently, mobile CMSs have appeared with tasks that exploit the mobility and the location of workers. For example, if a third party system requires a picture of a given place, it may publish a task asking for some worker to go there, take this picture and upload it. One problem of CMSs is that the more tasks they have, the harder it is for workers to find and choose one they are interested in. Besides, workers who accomplish tasks may have no particular experience and consequently provide bad results for tasks. In order to improve the matching between workers and spatio-temporal tasks in mobile CMSs, we propose a conceptual framework that consists of two mechanisms. One considers the requirements of a task for selecting suitable workers, while the other recommends tasks for a worker according to his preferences and skills. As a result, workers spend less time searching tasks, more working on it, providing results with higher quality.
Year
DOI
Venue
2014
10.1145/2675316.2675319
MobiGIS
Keywords
Field
DocType
algorithms,design,spatial databases and gis,mobile crowdsourcing,recommender systems,crowdsourcing market systems,spatial crowdsourcing,task matching
Recommender system,Publication,Data mining,Computer science,Crowdsourcing,Upload,Exploit,Third party,Market system,Conceptual framework
Conference
Citations 
PageRank 
References 
4
0.43
23
Authors
3
Name
Order
Citations
PageRank
André Sales Fonteles1132.88
Sylvain Bouveret271.51
Jérôme Gensel338050.95