| dc.description.abstract | Most recruitment advertisements are advertised on job search websites in an imprecise way. In order to have more exposure, it is possible to increase exposure through display advertisements on job search websites for expanding their reach.
The number of job application resumes is used as the basis for measuring the exposure effect. When the customers get fewer job applications, it may cause customer complaints and compensate customers. Accurate advertising is one of the ways to increase exposure to target groups.
This study uses machine learning to predict the behavior of job seekers applying for jobs on job search websites, and explores the differences between unpopular and popular times when there are more and less people applying. Besides, this study compares the results of four algorithms, namely Random Forest, Support Vector Machine, Logistic Regression and Bayesian classifier in the prediction classification, and combines two feature selection of principal component analysis and information gain to compare the two methods which could help in prediction.
In the experimental results, the Macro-F1and the recall rate of Logistic regression and random forest in the cold and hot period are both over 64%, and the AUC are 70%. Except for the support vector machine, the precision of others is all over 60%. Furthermore, Logistic regression and random forest are consistent in the trend of unpopular and popular periods. The relevant descriptions are as follows.
1. Random forest has the recall of 70% without applied job, and 65% with applied job. Logistic regression has the recall of 80% without applied job , and 40% with applied job.
2. The precision of random forest with applied jobs is 48%, and without applied jobs is 80%. The precision of Logistic regression is similar to Bayesian classifier and support vector machine, which is 50% without applied jobs and 40%with applied jobs. | en_US |