Publication Type : Conference Proceedings
Publisher : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE
Source : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Bangalore, India, India, pp. pp. 286-292., 2018
Url : https://ieeexplore.ieee.org/document/8554475
Keywords : computational modeling, Crowdsourcing, inexpertise worker, Libraries, mathematical model, skill heirarchy, Software, Task analysis, task personalization, task recommendation, Taxonomy
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : No
Year : 2018
Abstract : Crowdsourcing is an emerging technology which enables human workers to perform the task that cannot be done using automated tools. The crucial constituent of crowdsourcing platform is human workers. Since crowdsourcing platforms are overcrowded, workers find difficulty in selecting a suitable task for them. Employing task recommendation systems could improve this situation. However, task recommendation for new and inexpert workers is not explored well. We address this problem by designing a task recommendation model using skill taxonomy and participation probability of existing expert workers. The proposed model is validated through experimentation with both real and synthetic dataset
Cite this Research Publication : A. R. Kurup and Dr. Sajeev G. P., “Task Personalization for Inexpertise Workers in Incentive Based Crowdsourcing Platforms”, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Bangalore, India, India, pp. pp. 286-292., 2018