Efficient Collaborative Crowdsourcing

We consider the problem of making efficient quality-timecost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

Authors

• Zhengxiang Pan
• Han Yu Nanyang Technological University, Singapore
• Chunyan Miao Nanyang Technological University
• Cyril Leung

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