Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing

Abstract : Besides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets.
Type de document :
Communication dans un congrès
WWW2016, Apr 2016, Montreal, Canada. 〈〉. 〈10.1145/2872427.2883070〉
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger
Contributeur : Panagiotis Mavridis <>
Soumis le : jeudi 19 mai 2016 - 16:55:07
Dernière modification le : jeudi 7 février 2019 - 16:16:27
Document(s) archivé(s) le : samedi 20 août 2016 - 10:24:17


Fichiers produits par l'(les) auteur(s)


Copyright (Tous droits réservés)



Panagiotis Mavridis, David Gross-Amblard, Zoltán Miklós. Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing. WWW2016, Apr 2016, Montreal, Canada. 〈〉. 〈10.1145/2872427.2883070〉. 〈hal-01306481〉



Consultations de la notice


Téléchargements de fichiers