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Pre-selection of Recruitment Candidates Using Case Based Reasoning

机译:使用基于案例推理的招聘候选人进行预选

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摘要

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.
机译:手动预先选择潜在候选人的成本已经上升,雇主正在寻找自动化候选人的预选方法来填补他们组织的空缺.JOB门户服务已被证明是互联网上最成功和最受欢迎的信息服务。通过选择更具选择性,尤其是从大型申请人,能够减少雇用糟糕的个人的可能性。建议的系统使雇主能够更快地选择合适的候选人,并通过互联网招聘具有更快的循环的优势时间,更便宜,并为雇主和工作seekers.However双方更为方便,大多数的工作门户网站的搜索引擎依靠精确匹配检索constraints.For这种类型匹配的,以规则为基础的做法是正常utilized.To为此,使用CBR引擎的原型称为jobmatching以用于匹配目的,已开发,验证和评估.JobMatching©System Co毕业生数据库的MPrises以及包含基于案例的推理以增加其竞争优势的匹配引擎.CBR建议使用相似度测量的工作要求的最佳候选者。基于反馈,jobmatching©系统有助于预选候选人就业。

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