Abstract
Compensation management has been an important topic in the field of human resources. Job seekers want to get generous benefits for themselves, on the other hand companies want to use the least amount of cost to obtain high-value talents. However, the considerations of compensation are difficult to quantify. There are currently no effective evaluation method about pay level. It leads cognitive differences between job seekers and companies. Therefore, compensation forecast needs complete a lot of information provided. Through the Job Bank Big Data analytics, construct an objective compensation forecast model can achieve more discreet prediction effect. The study aims to develop a data mining and Big Data analytics framework for compensation forecast. It integrates random forest and decision tree technology and constructs a compensation management models to explore each job impact factors of the compensation. The study cooperates with a Taiwanese indicative Job Bank Web site for empirical research. Through historical data mean absolute error validates the method validity and improve forecast accuracy. The 140 jobs average forecast accuracy can increase effectively. Highest education level promoted 8.67%, job position promoted 8.30%, comapany size promoted 9.08% and industry category promoted 9.61%. It enhances the entirety predictive accuracy 8.92%.