From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems

TL;DR

This paper develops methods for automatically classifying and measuring semantic similarity of micro-tasks in crowdsourcing, aiding recommender systems to better match tasks with users based on semantic features.

Contribution

It introduces a framework for automatic task classification and similarity measurement based on task descriptions, focusing on semantic aspects rather than factual details.

Findings

Automatic classification of task descriptions is feasible.

Semantic similarity measures can effectively cluster micro-tasks.

Supports improved task recommendation in crowdsourcing systems.

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.