TaskRec: probabilistic matrix factorization in task recommendation in crowdsourcing systems

Authors:
Man-Ching Yuen;Irwin King;Kwong-Sak Leung
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

In crowdsourcing systems, task recommendation can help workers to find their right tasks faster as well as help requesters to receive good quality output quicker. However, previously proposed classification approach does not consider the dynamic scenarios of new workers and new tasks in the system. In this paper, we propose a Task Recommendation (TaskRec) framework based on a unified probabilistic matrix factorization, aiming to recommend tasks to workers in dynamic scenarios. Unlike traditional recommendation systems, workers do not provide their ratings on tasks in crowdsourcing systems, and thus we propose to transform worker behaviors into ratings. Complexity analysis shows that our framework is efficient and is scalable to large datasets. Finally, we conduct experiments on real-world datasets for performance evaluation.