知识密集型众包的优化
Optimization in Knowledge-Intensive Crowdsourcing
摘要：
我们提出SmartCrowd，优化协作知识密集型众包的框架。SmartCrowd在向员工分配任务的过程中考虑了人为因素，从而脱颖而出。人的因素指的是工人在不同技能方面的专长、他们的预期最低工资以及他们的可用性。在SmartCrowd中，我们将任务分配制定为优化问题，并依赖于预索引工人和自适应地维护索引，以这种方式，任务分配过程在定性和计算时间方面都得到优化。我们提出了严格的理论分析的优化问题，并提出了最佳和近似算法。最后，我们使用真实数据和合成数据进行了广泛的性能和质量实验，以证明SmartCrowd中的自适应索引对于实现高效、高质量的任务分配是必要的。
英文摘要：
We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers. Human factors designate workers' expertise in different skills, their expected minimum wage, and their availability. In SmartCrowd, we formulate task assignment as an optimization problem, and rely on pre-indexing workers and maintaining the indexes adaptively, in such a way that the task assignment process gets optimized both qualitatively, and computation time-wise. We present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms. We finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in SmartCrowd is necessary to achieve efficient high quality task assignment.
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