摘要
【目的/意义】随着互联网的不断普及,海量就业信息资源持续涌现,为了能够为待就业学生提供更具针对性的就业指导,满足更多高校学生的就业需要,本文提出基于大数据挖掘技术的学生就业需求信息资源采集研究。【方法/过程】首先,本文基于网络信息爬取模块、就业需求信息挖掘模块、资源整合模块对就业需求信息资源采集总体架构进行优化;并结合就业专业-就业岗位-就业需求信息-高校信息的组织体系,实现就业岗位和就业需求信息以及高校信息间的信息整合。然后,利用就业需求信息挖掘统一计算系统对已整合结果进行内容标准化处理,再利用深度学习算法分别进行就业异常数据筛查和特定异常就业数据特征识别,构建就业需求信息资源采集系统。最后,凭借E-S-Qual量表对系统中的就业信息采集质量进行评价。【结果/结论】结果表明,所提出的研究方法可以向用户推送全面的就业需求信息,并能全面分析需求信息推荐效果,有效为待就业学生提供相关就业指导。【局限/创新】但由于本次实证研究较单一,研究结果存在一定的局限性,日后可结合更多案例对所提方法进行验证,使结果更具说服力。
【Purpose/significance】With the continuous popularization of the Internet,massive employment information resources continue to emerge.In order to provide more targeted employment guidance for students to be employed and meet the employment needs of more college students,this paper proposes a research point on the collection of information resources for students'employment needs based on big data mining technology.【Method/process】Firstly,this article optimizes the overall architecture of employment demand information resource collection based on the network information crawling module,employment demand information mining module,and resource integration module;And combine the organizational system of employment majors,employment positions,employment demand information,and university information to achieve information integration between employment positions,employment demand information,and university information.Then,the unified calculation system for employment demand information mining is used to standardize the content of the integrated results,and deep learning algorithms are used to screen employment abnormal data and identify specific abnormal employment data features,respectively,to construct an employment demand information resource collection system.Finally,evaluate the quality of employment information collection in the system using the E-S-Qual scale.【Result/conclusion】The results show that the proposed research method can push comprehensive employment demand information to users,comprehensively analyze the effect of demand information recommendation,and effectively provide relevant employment guidance for students to be employed.【Innovation/limitation】However,due to the single empirical research,the research results have some limitations.In the future,more cases can be used to verify the proposed methods,making the results more convincing.
作者
卫善春
WEI Shanchun(Student Career Center,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《情报科学》
CSSCI
北大核心
2023年第9期130-137,共8页
Information Science
基金
2022年上海高校毕业生就业创业工作专项研究项目“一流高校毕业生到重点行业、关键领域就业现状及引导机制研究”(2022JYCYZX02)
2020年教育部思政创新发展中心研究课题“价值引领视阈下精准就业的网络实践与探究”(DYF-SJ-2020022)。
关键词
大数据挖掘
学生就业
需求信息
资源采集
网络爬取技术
相似主题判断数据挖掘算法
big data mining
student employment
demand information
resource collection
network crawling technology
data mining algorithm for similar topic judgment
