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Task Personalization for Inexpertise Workers in Incentive Based Crowdsourcing Platforms

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

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