From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems
Information Retrieval
2017-07-21 v1 Computation and Language
Abstract
Task selection in micro-task markets can be supported by recommender systems to help individuals to find appropriate tasks. Previous work showed that for the selection process of a micro-task the semantic aspects, such as the required action and the comprehensibility, are rated more important than factual aspects, such as the payment or the required completion time. This work gives a foundation to create such similarity measures. Therefore, we show that an automatic classification based on task descriptions is possible. Additionally, we propose similarity measures to cluster micro-tasks according to semantic aspects.
Cite
@article{arxiv.1707.06562,
title = {From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems},
author = {Steffen Schnitzer and Svenja Neitzel and Christoph Rensing},
journal= {arXiv preprint arXiv:1707.06562},
year = {2017}
}
Comments
Work in Progress Paper at HCOMP 2017