Title

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

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