A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction
Qingxin Meng (Rutgers- the State University of New Jersey);Hengshu Zhu (Baidu);Keli Xiao (Stony Brook University);Le Zhang (School of Computer Science, University of Science and Technology of China);Hui Xiong (Rutgers- the State University of New Jersey);
The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the employee turnover rate), there are no effective solutions for supporting the understanding of job mobility at an individual level. To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the next employer? 2) how long will the individual work in the new position? Specifically, our model exploits a hierarchical neural network structure with embedded attention mechanism for characterizing the internal and external job mobility. Also, it takes personal profile information into consideration in the learning process. Finally, the extensive results on real-world data show that the proposed model can lead to significant improvements in prediction accuracy for the two aforementioned prediction problems. Moreover, we show that the above two questions are well addressed by our model with a certain level of interpretability. For the case studies, we provide data-driven evidence showing interesting patterns associated with various factors (e.g., job duration, firm type, etc.) in the job mobility prediction process.
How can we assist you?
We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!
Please enter the word you see in the image below: