Abstract: Predicting job turnover among early career university graduates is crucial for both employees and employers. This study introduced a Feature Interaction based Neural Network model designed to predict job turnover among university graduates in their 20s and 30s in South Korea within the first five years of employment. The FINN model leveraged the Graduates Occupational Mobility Survey dataset, which included detailed information on approximately 26,544 graduates. This rich dataset encompassed a wide range of variables, including personal attributes, employment characteristics, job satisfaction, and job preparation activities. The model combined an embedding layer to convert sparse features into dense vectors with a neural network component to capture high-order feature interactions. We compared the FINN model's performance against eight baseline models: Logistic Regression, Factorization Machines, Field-aware Factorization Machines, Support Vector Machine, Random Forest, Product-based Neural Networks, Wide & Deep, and DeepFM. Evaluation metrics used were Area Under the ROC Curve (AUC) and Log Loss. The results demonstrated that the FINN model outperformed all baseline models, achieving an AUC of 0.830 and a Log Loss of 0.370. The FINN model represents a significant advancement in predictive modeling for job turnover, providing valuable insights that can inform both individual career planning and organizational human resource practices. This research underscores the potential of advanced neural network architectures in employment data analysis and predictive modeling.

Haewon Byeon, “A Feature Interaction Based Neural Network Approach: Predicting Job Turnover in Early Career Graduates in South Korea” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150833

@article{Byeon2024,
title = {A Feature Interaction Based Neural Network Approach: Predicting Job Turnover in Early Career Graduates in South Korea},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150833},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150833},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Haewon Byeon}
}