计算机工程与应用2017，Vol.53Issue(18)：141-148,270,9.DOI:10.3778/j.issn.1002-8331.1612-0406
中文简历自动解析及推荐算法
Chinese resume information automatic extraction and recommendation algorithm
摘要
Abstract
In order to solve the problem of laborious and time-consuming artificial selection from mass electronic resumes, a solution to resumes automatic extraction and recommendation is proposed. Firstly, the sentences in Chinese resume are represented as vectors through word segmentation, part-of-speech tagging and other preprocessing steps, then SVM classi-fication algorithm is used to classify the sentences into six predefined general classes, such as personal basic information, job intension, working experience and so on. Secondly, according to the lexical and grammatical features of personal basic information block, the rules are constructed by hand to extract the key information like Name, Gender, and Contact information. While the HMM model is used to extract the detailed information in complex information blocks, and puts forward rules and statistics based resume information extraction method. Finally, a Content-Based Reciprocal Recom-mender algorithm(CBRR)is proposed, which takes into account the preferences of both enterprise and job seekers. The experiment results show that the solution proposed in this paper can assist enterprises in recruitment, improve screening efficiency and save recruitment costs.关键词
信息抽取/推荐/协同过滤/规则/统计/简历Key words
information extraction/recommendation/collaborative filtering/rule/statistics/resume分类
信息技术与安全科学引用本文复制引用
谷楠楠,冯筠,孙霞,赵妍,张蕾..中文简历自动解析及推荐算法[J].计算机工程与应用,2017,53(18):141-148,270,9.基金项目
陕西省教育厅自然科学基金(No.JD11258) （No.JD11258）
陕西省教育厅科学研究计划自然科学专项项目(No.15JK1738) （No.15JK1738）
陕西省自然科学基础研究计划项目支撑(No.2015JQ6240) （No.2015JQ6240）
西北大学研究生课程建设项目(No.YJD15003). （No.YJD15003）
