TaskRec: Probabilistic Matrix Factorization in Task Recommendation in Crowdsourcing Systems

Citation data:

Neural Information Processing, ISSN: 0302-9743, Vol: 7664 LNCS, Issue: PART 2, Page: 516-525

Publication Year:
2012
Usage 2
Abstract Views 2
Captures 3
Readers 3
Citations 9
Citation Indexes 9
Repository URL:
http://repository.vtc.edu.hk/ive-it-sp/33
DOI:
10.1007/978-3-642-34481-7_63
Author(s):
Yuen, Man Ching, Connie; King, Irwin; Leung, Kwong Sak
Publisher(s):
Springer Nature; VTC Institutional Repository
Tags:
Mathematics; Computer Science; Crowdsourcing; Task recommendation; Matrix factorization; Computer Sciences
book chapter description
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. © 2012 Springer-Verlag.