  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.
