基于大数据分析的高校云招聘信息个性化推送研究  

Research on Personalized Push of University Cloud Recruitment Information Based on Big Data Analysis

作　　者：王金威 Wang Jinwei(Minnan University of Science and Technology,Quanzhou 362700,China)

机构地区：[1]闽南理工学院招生就业处,福建泉州362700

出　　处：《安徽电子信息职业技术学院学报》2022年第4期25-31,共7页Journal of Anhui Vocational College of Electronics & Information Technology

摘　　要：以向高校待就业学生推送企业招聘信息为目的,研究基于大数据分析的高校云招聘信息个性化推送方法。使用文本语义分块算法将高校云平台内的学生简历信息分块后,使用词频-逆文档算法获取学生简历信息的词法与语法特征向量。依据词法与语法特征向量建立学生信息抽取规则,并依据该规则依次抽取学生简历分块信息后,使用大数据分析算法的相似度计算方法获取企业招聘偏好相关性、待就业学生偏好相关性以及二者之间互惠相关性,以此构建全局偏好互惠推送模型,利用该模型生成高校云招聘信息个性化推送列表,实现高校云招聘信息个性化推送。实验表明,该方法提取到学生简历信息的F1数值始终高于0.83,且推送的招聘信息有效性好,应用后可有效提升高校应届毕业生就业率。In order to push enterprise recruitment information to college students,this paper studies the personalized push method of college cloud recruitment information based on big data analysis.The text semantic segmentation algorithm is used to block the student resume information in the cloud platform of colleges and universities,and word frequency-inverse document algorithm is used to obtain the lexical and grammatical feature vectors of the student resume information.Based on the lexical and grammatical features vector to establish student information extraction rules,and according to the rules extraction in turn students resume block information,using big data analysis algorithm of similarity calculation method for hiring preference correlation,underemployed students preference correlation and mutual correlation between them,in order to build a global preferences reciprocal push model,the model is used to generate the personalized push list of college cloud recruitment information to realize the personalized push of college cloud recruitment information.The experiment shows that the F1 value of the student resume information extracted by this method is always higher than 0.83,and the recruitment information pushed is effective,and the application can effectively improve the employment rate of college graduates.

关 键 词：大数据分析 高校云 招聘信息 语义块 信息抽取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]