From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems
Key: SNR17-1
Author: Steffen Schnitzer, Svenja Neitzel, Christoph Rensing
Date: July 2017
Kind: In proceedings
Publisher: KOM TU-Darmstadt
Book title: Presented at the 5th Conference on Human Computation and Crowdsourcing (HCOMP 2017)
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.
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