Crowdsourcing has become an efficient measure to solve machine-hard problems by embracing group wisdom, in which tasks are disseminated and assigned to a group of workers in the way of open competition. The social relationships formed during this process may in turn contribute to the completion of future tasks. In this sense, it is necessary to take social factors into consideration in the research of crowdsourcing. However, there is little work on the interactions between social relationships and crowdsourcing currently. In this paper, we propose to study such interactions in those social-oriented crowdsourcing systems from the perspective of task assignment. A prototype system is built to help users publish, assign, accept, and accomplish location-based crowdsourcing tasks as well as promoting the development and utilization of social relationships during the crowdsourcing. Especially, in order to exploit the potential relationships between crowdsourcing workers and tasks, we propose a “worker-task” accuracy estimation algorithm based on a graph model that joints the factorized matrixes of both the user social networks and the history “worker-task” matrix. With the worker-task accuracy estimation matrix, a group of optimal worker candidates is efficiently chosen for a task, and a greedy task assignment algorithm is proposed to further the matching of worker-task pairs among multiple crowdsourcing tasks so as to maximize the overall accuracy. Compared with the similarity based task assignment algorithm, experimental results show that the average recommendation success rate increased by 3.67%; the average task completion rate increased by 6.17%; the number of new friends added per week increased from 7.4 to 10.5; and the average task acceptance time decreased by 8.5 seconds.

众包已经成为通过拥抱集体智慧来解决机器难题的有效措施，在集体智慧中，任务是通过公开竞争的方式进行分配并分配给一组工人的。在此过程中形成的社会关系反过来可能有助于完成未来的任务。从这个意义上讲，在众包研究中必须考虑社会因素。但是，目前关于社会关系和众包之间的互动的工作很少。在本文中，我们建议从任务分配的角度研究那些面向社会的众包系统中的这种相互作用。建立了一个原型系统来帮助用户发布，分配，接受，并完成基于位置的众包任务，并在众包过程中促进社会关系的发展和利用。特别是，为了利用众包工作者和任务之间的潜在关系，我们提出了一种基于图模型的“工作者-任务”准确性估计算法，该模型将用户社交网络和历史“工作者-任务”的分解矩阵结合在一起。矩阵。利用工作人员任务准确性估计矩阵，可以有效地选择一组最佳工作人员候选任务，并提出一种贪婪的任务分配算法，以进一步匹配多个众包任务中的工作人员任务对，从而最大程度地提高整体准确性。 。与基于相似度的任务分配算法相比，实验结果表明，平均推荐成功率提高了3.67％；平均任务完成率提高6.17％；每周增加的新朋友数量从7.4增加到10.5；平均任务接受时间减少了8.5秒。