基于BP神经网络的就业招聘企业客户分类问题研究

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 1-4.

• 智能计算 • 下一篇

基于BP神经网络的就业招聘企业客户分类问题研究

乔非,葛彦昊

同济大学电子与信息工程学院CIMS中心上海200092,同济大学电子与信息工程学院CIMS中心上海200092

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受同济大学校园信息化项目资助

Customer Classification Model of Employers by Using BP Neural Networks

QIAO Fei and GE Yan-hao

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 在高校就业信息化建设中,对进入高校招聘毕业生的企业客户进行等级分类和预测能够有效帮助高校管理者评估与该企业的合作效用,推动大学生就业信息化服务向更具针对性的个性化推荐方向发展。目前该问题的解决方案大多基于从业人员的主观经验判断,缺乏完善的定量分析模型。抽取某高校教育管理信息系统中与进校招聘企业客户相关的数据样本,并借助BP神经网络模型搭建有效的数据分析模型,使用训练样本数据训练模型从而确定分析模型中各节点参数,将训练后的分析模型用于预测测试样本集得到最终的性能指标,最后将该模型的分类性能与当前同类问题的其他解决方案进行比较。对比结果显示,基于BP神经网络模型的分类方式在预测准确度和精度方面明显优于其他现有模型。该研究成果能够在信息化平台中为该问题提供高效的解决方案,帮助高校就业工作管理者及服务人员依靠该模型对进校招聘企业做出快速精准的客户等级预测,为高校就业服务工作决策提供支持。

关键词: BP人工神经网络,数据挖掘,分类问题,就业信息服务

Abstract: Customer classification of employers brings great benefits for correctly evaluating the category of each employers,which helps decision-makers to appraise the corporation efficiency when they corporate with each customer.In order to give quantitative model to help solving this problem,we extracted raw data series from the educational management information system and built a classification calculating model based on BP neural network.Then we managed to get each value of the parameters in the model by training the historical datasets and compared the result of prediction with other methods which are widely used nowadays to solve this problem.Finally we made a conclusion that customer classification of employers for career guiding service by using BP neural network model performs better than other existing solutions and gives more efficient supports to management layer of universities and government by making comparably precise predictions.

Key words: BP neural network,Data mining,Data classification problem,Career guiding information service

乔非,葛彦昊. 基于BP神经网络的就业招聘企业客户分类问题研究[J]. 计算机科学, 2015, 42(Z11): 1-4. https://doi.org/

QIAO Fei and GE Yan-hao. Customer Classification Model of Employers by Using BP Neural Networks[J]. Computer Science, 2015, 42(Z11): 1-4. https://doi.org/

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