{"title":"Pre-selection of Recruitment Candidates Using Case Based Reasoning","authors":"Fadzilah Siraj, Norhashimah Mustafa, Megat Firdaus Haris, Shahrin Rizlan Mohd Yusof, Muhammad Ashraq Salahuddin, Md. Rajib Hasan","doi":"10.1109/CIMSIM.2011.24","DOIUrl":null,"url":null,"abstract":"The cost of manually preselecting potential candidates have risen and employers are searching for methods to automate the pre-selection of candidates to fill in the vacancies at their organizations.Job portal services have proved to be the most successful and popular information services on the internet.Being more selective through selection especially from large pool of applicants will be able to decrease the possibility of hiring the poor-performing individuals.The proposed system enables the employer to select the right candidate quicker and by using internet for recruitment has the advantage of faster cycle time, cheaper, and more convenient for both the employers and the job seekers.However, most job portal search engines rely on exact-match retrieval constraints.For this type of matching, a rule based approach was normally utilized.To this end, a prototype called JOBMatching© using CBR engine for matching purposes has been developed, validated and evaluated.JOBMatching© system comprises of databases of graduates as well as a match engine that incorporates Case Based Reasoning to increase its competitive advantage.CBR recommends the best candidate suitable with the job requirement using similarity measurement.Based on the feedback, JOBMatching© system facilitates the pre-selection of candidates for employment.","PeriodicalId":125671,"journal":{"name":"2011 Third International Conference on Computational Intelligence, Modelling & Simulation","volume":"125 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Third International Conference on Computational Intelligence, Modelling & Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMSIM.2011.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
基于案例推理的招聘候选人预选
人工预选潜在候选人的成本已经上升,雇主们正在寻找方法来自动预选候选人来填补他们组织的空缺。招聘门户网站服务已被证明是互联网上最成功和最受欢迎的信息服务。通过选拔,特别是在大量申请者中进行更严格的筛选,将能够减少雇用表现不佳的个人的可能性。提出的系统使雇主能够更快地选择合适的候选人,并通过使用互联网进行招聘,具有更快的周期时间,更便宜,更方便雇主和求职者的优势。然而,大多数工作门户搜索引擎依赖于精确匹配检索约束。对于这种类型的匹配,通常使用基于规则的方法。为此,开发、验证和评估了一个名为JOBMatching©的原型,该原型使用CBR引擎进行匹配。JOBMatching©系统包括毕业生数据库以及结合基于案例推理的匹配引擎,以增加其竞争优势。CBR使用相似度测量推荐最适合职位要求的候选人。JOBMatching©系统根据反馈信息,方便求职者的就业预选。
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