Exploiting the Contagious Effect for Employee Turnover Prediction


  • Mingfei Teng Rutgers University
  • Hengshu Zhu Baidu Inc.
  • Chuanren Liu Drexel University
  • Chen Zhu Baidu Inc.
  • Hui Xiong The State University of New Jersey




Talent turnover often costs a large amount of business time, money and performance. Therefore, employee turnover prediction is critical for proactive talent management. Existing approaches on turnover prediction are mainly based on profiling of employees and their working environments, while the important contagious effect of employee turnovers has been largely ignored. To this end, in this paper, we propose a contagious effect heterogeneous neural network (CEHNN) for turnover prediction by integrating the employee profiles, the environmental factors, and more importantly, the influence of turnover behaviors of co-workers. Moreover, a global attention mechanism is designed to evaluate the heterogeneous impact on potential turnover behaviors. This attention mechanism can improve the interpretability of turnover prediction and provide actionable insights for talent retention. Finally, we conduct extensive experiments and case studies on a realworld dataset from a large company to validate the effectiveness of the contagious effect for turnover prediction.




How to Cite

Teng, M., Zhu, H., Liu, C., Zhu, C., & Xiong, H. (2019). Exploiting the Contagious Effect for Employee Turnover Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1166-1173. https://doi.org/10.1609/aaai.v33i01.33011166



AAAI Technical Track: Applications