计算机与现代化 ›› 2021, Vol. 0 ›› Issue (07): 60-64.

• 数据库与数据挖掘 • 上一篇    下一篇

基于大数据技术的人才智能推荐方法

  

  1. (北京市中小企业信用再担保有限公司,北京100010)
  • 出版日期:2021-08-02 发布日期:2021-08-02
  • 作者简介:魏云东(1970—),男,山东青岛人,高级人力资源管理师,硕士,研究方向:人力资源应用,E-mail: wen2613498@163.com。

Intelligent Talent Recommendation Method Based on Big Data Technology

  1. (Beijing SME Credit Reguarantee Co.  Ltd., Beijing 100010, China)
  • Online:2021-08-02 Published:2021-08-02

摘要: 复杂多样的岗位信息使得很多求职人员很难查找到适合自己的岗位信息,为了提高人力资源的推荐质量,本文基于梯度提升树和混合卷积神经网络设计一种有针对性的人才市场推荐模型。利用流式分布式方法收集求职人员信息并将其转换为可用于算法分析的独热编码,使用梯度提升树提取求职人员特征。混合卷积神经网络在经过训练之后可实现有针对性的人才推荐。本文模型与不结合梯度提升树的混合卷积神经网络、结合梯度提升树的卷积神经网络相比,在召回率和F1-Score上分别提高了9.78%和10.1%。这说明,结合梯度提升树的混合卷积神经网络算法能够有效提高人力资源的推荐质量。

关键词: 大数据技术, 梯度提升树, 神经网络, 人才推荐

Abstract: Complex and diverse job information makes it difficult for many job seekers to find suitable job information. In order to improve the quality of human resource recommendation, this paper designs a targeted talent market recommendation model based on gradient lifting tree and hybrid convolution neural network. The flow distributed method is used to collect the information of job seekers and convert it into a unique hot code which can be used for algorithm analysis. The gradient lifting tree is used to extract the features of job seekers. After training, the hybrid convolution neural network can achieve targeted talent recommendation. Compared with the hybrid convolution neural network without gradient lifting tree and the convolution neural network with gradient lifting tree, the recall rate and F1 score of this model are improved by 9.78% and 10.1% respectively. This shows that the hybrid convolution neural network algorithm combined with gradient lifting tree can effectively improve the quality of human resource recommendation.

Key words: big data technology, gradient lifting tree, neural network, talent recommendation