Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece- workers", have emerged as an effective paradigm for human- powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all crowdsourcers must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in some way such as majority voting. In this paper, we consider a model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, based on low-rank matrix approximation, significantly outperforms majority voting and, in fact, is order-optimal through comparison to an oracle that knows the reliability of every worker.
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