ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (7): 1408-1419.doi: 10.7544/issn1000-1239.2019.20180674

• 人工智能 • 上一篇    下一篇



  1. (西北工业大学计算机学院 西安 710072) (
  • 出版日期: 2019-07-01
  • 基金资助: 

The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns

Xu Huang, Yu Zhiwen, Guo Bin, Wang Zhu   

  1. (School of Computer Science, Northwestern Polytechnical University, Xi’an 710072)
  • Online: 2019-07-01

摘要: 随着经济全球化的发展,地区间的人才流动日益频繁,人才的引进和流失对各地区的科技和经济的发展产生了巨大的影响.对人才流动问题进行深入研究,是实现有效的人才流动监控、制定科学人才引流政策的基础.提出一种数据驱动的人才流动分析方法,探究地区间人才流动的规律,并预测未来的人才流动.具体而言,用基于矩阵序列的定量方法表示地区间人才流动现象,并分析地区间人才流动的时空模式以及地区人才吸引力的差异和人才交换的聚集效应.进一步提出人才流动预测模型,结合卷积和循环神经网络实现地区间人才流量的预估.通过大规模在线职业平台的数据对所提出的模型进行验证,实验表明:提出的模型误差相对基准模型平均降低约15%.

关键词: 人才流动, 时空模式, 深度学习模型, 聚类, 序列预测

Abstract: With the development of economic globalization, the exchange of talents among cities has become increasingly frequent. Brain drain and brain gain have had a tremendous impact on the development of technology and the economy. An in-depth study of the regularities of talent mobility is the basis for the monitoring of talent exchange and the formulation of a scientific talent flow policy. To this end, in this paper, we propose a data-driven talent mobility analysis method to study the patterns of talent exchange among cities and to forecast the future mobility. Specifically, we leverage a data structure named talent mobility matrix sequence, to represent and mine the temporal-spatial patterns of inter-regional talent mobility. The comparison of attractiveness for talents among different cities is analyzed based on the talent flows. Further, we propose a talent flow prediction model based on the combination of both convolution and recurrent neural networks to forecast regional talent flows. Theoretically, the model can alleviate the data sparsity problem as well as reduce the scale of parameters compared with traditional regression models. The model was validated by a large scale of data collected from an online professional network. Experimental results show that the proposed model reduces the error by 15% on average compared with benchmark models.

Key words: talent mobility, spatial-temporal pattern, deep learning models, clustering, sequence prediction