Abstract

In order to have a better study of pricing of crowdsourcing APP tasks, mission data was collected about an already completed project, including the location, pricing and fulfillment of each mission. Get the law of the task pricing of the completed projects by analyzing the location distribution of the task points and the members, and then analyze the reason of the task failure. Besides, combine the information data of the members to improve the original pricing model based on the K-means clustering. Focus on the task package pricing, design a better task pricing model to improve the success rate of the task.

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