Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060503.
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
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.
 Zhang Z., Kui J., Xie X., Zhou Y. Crowdsourcing quality control strategy and evaluation algorithm. Chinese Journal of Computers, 36(08).1636-1649 (2013).
 Li S., Wei M., Huang S. Deep generative crowdsourcing learning using annotator correlation. Journal of Software, 33(4). 1274-1286 (2022).
 Drapeau R., Chilton L., Bragg J., Weld D. Microtalk: Using argumentation to improve crowdsourcing accuracy. In. Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing, vol. 4, pp. 32–41(2016).
 Chittilappilly A. I., Chen L., Amer-Yahia S. A survey of general-purpose crowdsourcing techniques. IEEE Transactions on Knowledge and Data Engineering 28(9), 2246–2266 (2016).
 Liu D., Hu H., Wu D. Weighted Network Modeling and Module Partitioning Among Crowdsourcing Design Tasks for Social Product Development. Industrial Engineering Journal, 24(5). 95-100 (2021).
 Jiang J., An B., Jiang Y., Lin D., Bu Z., Cao J., Hao Z. Understanding crowdsourcing systems from a multiagent perspective and approach. ACM Transactions on Autonomous and Adaptive Systems 13(2), 1–32 (2018).
 Wang W., Jiang J., An B., Jiang Y., Chen B. Toward efficient team formation for crowdsourcing in noncooperative social networks. IEEE Transactions on Cybernetics 47(12), 4208–4222 (2017).
 Jiang J., An B., Jiang Y., Zhang C., Bu Z., Cao J. Group-oriented task allocation for crowdsourcing in social networks. IEEE Transactions on Systems, Man, and Cybernetics. Systems 51(7), 4417–4432 (2021).
 Cheng P., Lian X., Chen L., Han J., Zhao J. Task assignment on multi-skill oriented spatial crowdsourcing. IEEE Transactions on Knowledge and Data Engineering 28(8), 2201–2215 (2016).
 Zhang J., Wei J. Research on Crowdsourcing-oriented Global Complex Task Assignment Based on Artificial Intelligence. 2022 2nd International Symposium on Artificial Intelligence and its Application on Media, pp.70-74(2022).