How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder
This work addresses the problem of setting effective monetary incentives for crowd workers, but it is incremental as it applies existing methods to a specific dataset without broader innovation.
The study tackled the challenge of validating monetary prize amounts for crowdsourcing tasks by comparing multiple linear regression, logistic regression, and K-nearest neighbor models on a TopCoder dataset, finding that logistic regression achieved the highest accuracy of 90% for price prediction.
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.