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hierarchical-career-path-aware-network

This code is used in the paper A hierarchical career-path-aware neural network for job mobility prediction

Reference

If you use this code or data as part of any published research, please acknowledge the following paper:

@inproceedings{10.1145/3292500.3330969,
author = {Meng, Qingxin and Zhu, Hengshu and Xiao, Keli and Zhang, Le and Xiong, Hui},
title = {A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3330969},
doi = {10.1145/3292500.3330969},
abstract = {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.},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {14โ€“24},
numpages = {11},
keywords = {survival analysis, job mobility prediction, career path},
location = {Anchorage, AK, USA},
series = {KDD '19}
}

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hierarchical-career-path-aware-network's Issues

Can you provide access to the dataset?

I am a graduate student pursuing my master degree, and have tried to use the official API for data crawling, but encountered many problems, and realized that it is difficult to construct a large amount of data such as the paper.

I wonder to know if it would be possible to obtain a copy of the dataset.
Truly grateful for any information you can provide.

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