基于Hadoop平台的大数据人力资源管理高效推荐研究

Research on Efficient Recommendation of Large Data Human Resource Management Based on Hadoop Platform

为了提高Hadoop平台下大数据人力资源管理推荐的高效性和精准性,采用支持向量机来完成岗位匹配。将人员指标要素样本进行稀疏表示,得到人员指标要素稀疏矩阵,经过支持向量机对样本进行二元分类,判断人员对岗位的匹配程度,最后引入随机变换函数,实现Hadoop平台下大数据环境下的动态推荐。经过实验证明:文中算法人员岗位匹配精准度好,且动态推荐效率高且能实现批量推荐。

In order to improve the efficiency and accuracy of large data human resource management recommendation based on Hadoop platform,support vector machine was used to complete job matching.The sparse representation of personnel indicators was used to get the sparse matrix of personnel indicators.The samples were classified by support vector machine,and the matching degree of personnel was judged.Finally,the random transformation function was introduced to realize the dynamic recommendation based on Hadoop platform in large data environment.Experiments showed that the proposed algorithm had good job matching accuracy,high dynamic recommendation efficiency and can achieve batch recommendation.