An Online Cost Sensitive Decision-Making Method in Crowdsourcing Systems

Crowdsourcing has created a variety of opportunities for many chal-lenging problems by leveraging human intelligence. For example, applications such as image tagging, natural language processing, and semantic-based information retrieval can exploit crowd-based human computation to supplement existing computational algo-rithms. Naturally, human workers in crowdsourcing solve problems based on their knowledge, experience, and perception. It is there-fore not clear which problems can be better solved by crowdsourc-ing than solving solely using traditional machine-based methods. Therefore, a cost sensitive quantitative analysis method is needed. In this paper, we design and implement a cost sensitive method for crowdsourcing. We online estimate the profit of the crowd-sourcing job so that those questions with no future profit from crowdsourcing can be terminated. Two models are proposed to estimate the profit of crowdsourcing job, namely the linear value model and the generalized non-linear model. Using these models, the expected profit of obtaining new answers for a specific question is computed based on the answers already received. A question is terminated in real time if the marginal expected profit of obtaining more answers is not positive. We extends the method to publish a batch of questions in a HIT. We evaluate the effectiveness of our proposed method using two real world jobs on AMT. The experi-mental results show that our proposed method outperforms all the state-of-art methods