Nowadays, online crowdsourcing services are quite common such as Amazon Mechanical Turk and Google Helpouts. For such online services, it is important to attract "workers" to provide high-quality solutions to the "tasks" outsourced by "requesters". We present a unified study of incentive and reputation mechanisms for online crowdsourcing systems. We first design an mechanism to incentivize workers provide their maximum effort, which allows multiple workers to solve a task, splits the reward among workers based on requester evaluations of the solution quality. We design a reputation mechanism, which ensures that low-skilled workers do not provide low-quality solutions by tracking workers' historical contributions, and penalizing those workers having poor reputation. We show that our incentive and reputation mechanisms are robust against human biases in solution quality evaluation.
['Hong Xie', 'John C. S. Lui', 'Donald F. Towsley']
Incentive and reputation mechanisms for online crowdsourcing systems