Abstract: Currently, most recruitment websites use keyword search or job nature classification to filter the salary information that job seekers are most concerned about. Job seekers need to spend much time and effort to understand the salary range of their desired position. In order to help job seekers quickly and accurately understand the salary of their desired position and market value, Word2vec model and latent Dirichlet allocation model are used to obtain topic features, which are used as the basis for the salary prediction model. The study uses deep neural networks and adaptive moment estimation algorithms to construct the salary prediction model. Based on the constructed salary prediction model, the final salary prediction system is constructed based on a browser/server model. The results showed that on the training set, the maximum accuracy of the salary prediction model was 96.71%, the minimum was 93.75%, and the average was 95.07%. The mean absolute percentage error and mean square error of this model were 5.661% and 0.3462, respectively. The maximum average response time of the salary prediction system was 134.2s, the minimum was 2.02s, and the maximum throughput was 1500000byte/s. The salary prediction model has good performance, which can provide technical support for salary prediction.

Yuping Wang, MingYan Bai and Changjiang Liao. “Precision Construction of Salary Prediction System Based on Deep Neural Network”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150770

@article{Wang2024,
title = {Precision Construction of Salary Prediction System Based on Deep Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150770},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150770},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Yuping Wang and MingYan Bai and Changjiang Liao}
}