论文标题
我应该付多少钱？ Topopoder货币奖的经验分析
How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder
论文作者
论文摘要
据报道，任务货币奖是吸引人群工人的最重要的激励因素之一。虽然使用基于专家的方法为众包任务进行定价是一种常见的做法，但验证不同任务的相关价格的挑战是一个不断的问题。为了解决这个问题，比较了多个线性回归，逻辑回归和K-Nearest邻居的三种不同分类，以找到最准确的预测价格，使用TopCoder网站的数据集。比较所选算法的结果表明，物流回归模型将提供90％的最高精度，以预测任务的相关价格，而KNN对K = 7的精度排名为64％。此外，应用PCA不会导致任何更好的预测准确性，因为数据组件不相关。
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.
