How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder
| Autor: | Saremi, Mostaan Lotfalian, Saremi, Razieh, Martinez-Mejorado, Denisse |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | C. Stephanidis and M. Antona (Eds.): HCII 2020, CCIS 1226, pp. 202-208, 2020 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-50732-9 |
| Popis: | It is reported that task monetary prize is one of the most important motivating factors to attract crowd workers. While using expert-based methods to price Crowdsourcing tasks is a common practice, the challenge of validating the associated prices across different tasks is a constant issue. To address this issue, three different classifications of multiple linear regression, logistic regression, and K-nearest neighbor were compared to find the most accurate predicted price, using a dataset from the TopCoder website. The result of comparing chosen algorithms showed that the logistics regression model will provide the highest accuracy of 90% to predict the associated price to tasks and KNN ranked the second with an accuracy of 64% for K = 7. Also, applying PCA wouldn't lead to any better prediction accuracy as data components are not correlated. Comment: 8 pages, 5 figures, 3 tables, HIC International 2020 |
| Databáze: | arXiv |
| Externí odkaz: |
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