• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
高级检索

人才流动的时空模式:分析与预测

胥皇, 於志文, 郭斌, 王柱

胥皇, 於志文, 郭斌, 王柱. 人才流动的时空模式:分析与预测[J]. 计算机研究与发展, 2019, 56(7): 1408-1419. DOI: 10.7544/issn1000-1239.2019.20180674
引用本文: 胥皇, 於志文, 郭斌, 王柱. 人才流动的时空模式:分析与预测[J]. 计算机研究与发展, 2019, 56(7): 1408-1419. DOI: 10.7544/issn1000-1239.2019.20180674
Xu Huang, Yu Zhiwen, Guo Bin, Wang Zhu. The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns[J]. Journal of Computer Research and Development, 2019, 56(7): 1408-1419. DOI: 10.7544/issn1000-1239.2019.20180674
Citation: Xu Huang, Yu Zhiwen, Guo Bin, Wang Zhu. The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns[J]. Journal of Computer Research and Development, 2019, 56(7): 1408-1419. DOI: 10.7544/issn1000-1239.2019.20180674

人才流动的时空模式:分析与预测

基金项目: 国家杰出青年科学基金项目(61725205);国家重点基础研究发展计划基金项目(2015CB352401);国家自然科学基金项目(61332005,61772428)
详细信息
  • 中图分类号: TP391

The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns

  • 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%.
    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.
  • 期刊类型引用(3)

    1. 王澍雅. 大连市高端人才流动问题及对策研究. 就业与保障. 2020(03): 116-118 . 百度学术
    2. 朱美峰. 基于全局主成分的山西省人才吸引力量化分析. 中北大学学报(社会科学版). 2020(06): 71-76 . 百度学术
    3. 郦苏菲,王杨,阮妹,王茜,杨卉. 全球热点城市科研人员流动性分析. 文献与数据学报. 2019(03): 45-55 . 百度学术

    其他类型引用(10)

计量
  • 文章访问数:  1290
  • HTML全文浏览量:  6
  • PDF下载量:  522
  • 被引次数: 13
出版历程
  • 发布日期:  2019-06-30

目录

    /

    返回文章
    返回