TaskRec: probabilistic matrix factorization in task recommendation in crowdsourcing systems
Document Type
Conference Proceeding
Publication Date
2012
Keywords
Crowdsourcing, Task recommendation, Matrix factorization
DOI
10.1007/978-3-642-34481-7_63
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.
Source Publication
The 19th International Conference on Neural Information Processing, 2012 November 12-15, Doha, Qatar. Proceedings, Part II
ISSN
0302-9743
ISBN
9783642344800
First Page
516
Last Page
525
Recommended Citation
Yuen, M.,King, I.,& Leung, K. (2012). TaskRec: probabilistic matrix factorization in task recommendation in crowdsourcing systems. The 19th International Conference on Neural Information Processing, 2012 November 12-15, Doha, Qatar. Proceedings, Part II, 516-525. http://dx.doi.org/10.1007/978-3-642-34481-7_63