Abstract
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 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.
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Lotfalian Saremi, M., Saremi, R., Martinez-Mejorado, D. (2020). How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-50732-9_27
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DOI: https://doi.org/10.1007/978-3-030-50732-9_27
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