问题思路：
一个用户会在公司内部职位变动，也会在公司与公司之间跳职，所以这里就有一个序列了，通过对这个序列进行建模，就可以预测用户下个职位在哪个公司了。
同时，用户在每个职位都有一定的任职时间，对于这个时间的预测，也是本文关系的问题。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction | |||||||||||
| 文献作者 | Qingxin Meng; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | |||||||||||
| 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. | |||||||||||
