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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 work-ers in the way of open competition.The social relationships formed during this process may in turn contribute to the com-pletion of future tasks.In this sense,it is necessary to take so-cial factors into consideration in the research of crowdsourc-ing.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 assign-ment.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 esti-mation algorithm based on a graph model that joints the factor-ized matrixes of both the user social networks and the history“worker-task”matrix.With the worker-task accuracy estima-tion 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 ac-curacy.Compared with the similarity based task assignment algorithm,experimental results show that the average recom-mendation 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.