[1]唐新晨. 基于认知计算的就业咨询智慧服务系统[J].计算机技术与发展,2017,27(11):166-170.
　TANG Xin-chen. Employment Consultation Intelligent Service System Based on Cognitive Computation[J].,2017,27(11):166-170.
点击复制

 基于认知计算的就业咨询智慧服务系统()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
166-170
栏目:
应用开发研究
出版日期:
2017-11-10

文章信息/Info

Title:
 Employment Consultation Intelligent Service System Based on Cognitive Computation
文章编号:
1673-629X（2017)11-0166-05
作者:
 唐新晨
 南京邮电大学 通信与信息工程学院
Author(s):
 TANG Xin-chen
关键词:
 ; 认知计算; Scrapy爬虫; 机器学习; Web应用; 服务系统
Keywords:
 ; cognitive computing; Scrapy; machine learning; Web application; service system
分类号:
TP302
文献标志码:
A
摘要:
 
随着智慧服务系统的发展和大数据时代的到来,如何实现类似人脑的认知与判决为应届生求职方向做出正确的决策,显得尤为重要.智慧服务系统由四部分组成,数据采集单元使用Scrapy爬虫框架获取信息,能够实时从各大招聘网站采集招聘信息;数据计算平台使用随机森林、SVM和朴素贝叶斯等基于认知计算的相关算法进行文本识别、特征提取以及文本分类等工作,能够正确实现特征采样和数据分类;数据存储单元搭建MongoDB数据库集群完成数据存储工作,具备海量数据储量能力和高容错性;用户服务平台由Web应用框架构建,具备多用户业务服务能力.因此其能够有效采集和分类招聘信息,准确定位学生能力,从而高效地为院校学生的就业岗位选择提供咨询与帮助.
Abstract:
 Currently,with the development of the intelligence service system and the arrival of the big data era,how to use the computer to help graduates make right decisions of job hunting like human is particularly important. Employment consultation intelligent service system with cognitive computation consists of four parts. Data collection unit uses the Scrapy framework for massive employee informa-tion from the various employee network in real-time. Data computing platform carries out the text recognition,feature extraction and text classification by several algorithms based on cognitive computing like random forest,SVM and Naive Bayes,which can correctly realize the feature sampling and data classification. Data storage unit builds the MongoDB cluster to complete the data storage with large memory capacity and high fault tolerance. User service platform integrates the Web framework and has multiple user services. Therefore,it can col-lect and classify effectively the employee information and evaluate students’ ability accurately,which can provide students for effective help on choosing the right and good job.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(11):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(11):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(11):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(11):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(11):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(11):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(11):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(11):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(11):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(11):47.

更新日期/Last Update: 2018-01-02