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Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060503.

Collective Assignment of Complex Crowdsourcing Tasks Based on the KM Algorithm


Jinwei Zhang

Corresponding Author:
Jinwei Zhang

School of Information Engineering, Nanjing University of Finance and Economics, Nanjing, China


With the increasing complexity of crowdsourcing scenarios, the assignment of complex tasks brings new challenges to the further application of crowdsourcing. Previous research work only focused on how to find a worker team that meets the task requirements, without comprehensively considering factors such as task and worker skill characteristics, time, task budget, and worker compensation. Thus, the success rate of task assignment is low. When assigning large-scale complex tasks, relatively complex tasks will not be able to find workers who meet the requirements, resulting in allocation failures. Thus, this paper studies a complex task-oriented collective assignment model to solve the problem that a large number of complex tasks cannot be assigned in crowdsourcing platforms while many workers have no tasks to do. In the model, the complex task assignment problem is mapped as a weighted bipartite graph matching problem, then the KM algorithm is used to solve the optimal assignment scheme. Finally, this paper conducts comparative experiments on real datasets, and the results show that the proposed model has better performance in terms of task success rate and task payment total cost.


Crowdsourcing, Complex task, Collective task allocation, KM algorithm

Cite This Paper

Jinwei Zhang. Collective Assignment of Complex Crowdsourcing Tasks Based on the KM Algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 14-20. https://doi.org/10.25236/AJCIS.2023.060503.


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