Task re-pricing model based on density-based spatial clustering of applications

Abstract

In the retail market, gathering marketing data is essential at different stages of advertisement and promotion; currently, this is achieved via online crowdsourcing. The jobs involving such tasks must be reasonably priced to attract part-time employees depending on the retail budget. Herein, a new approach is presented to enhance the task repricing performance of online crowdsourcing platforms using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, genetic general regression neural network (G-GRNN), and AdaBoost meta-algorithm. Initially, DBSCAN is used to analyze the agent task distribution, task density, and ambient agent credibility. Then, G-GRNN and AdaBoost are applied to reprice the tasks and evaluate the completeness of the repricing. Results show that the proposed method can optimize the repricing outcomes and enhance the efficiency of the repricing process. This study was conducted to address the crowdsourcing pricing problem from the perspective of firms. The results demonstrate the effectiveness of the machine learning methods (DBSCAN, G-GRNN, and AdaBoost) in solving the task repricing problem.