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dc.contributor.authorLi, Yung-Mingen_US
dc.contributor.authorLai, Cheng-Yangen_US
dc.contributor.authorKao, Chien-Pangen_US
dc.date.accessioned2014-12-08T15:29:39Z-
dc.date.available2014-12-08T15:29:39Z-
dc.date.issued2011-08-01en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10489-009-0204-9en_US
dc.identifier.urihttp://hdl.handle.net/11536/21294-
dc.description.abstractAdvances in information technology have led to behavioral changes in people and submission of curriculum vitae (CV) via the Internet has become an often-seen phenomenon. Without any technological support for the filtering process, recruitment can be difficult. In this research, a method combining five-factor personality inventory, support vector machine (SVM), and multi-criteria decision-making (MCDM) method was proposed to improve the quality of recruiting appropriate candidates. The online questionnaire personality testing developed by the International Personality Item Pool (IPIP) was utilized to identify the personal traits of candidates and both SVM and MCDM were employed to predict and support the decision of personnel choice. SVM was utilized to predict the fitness of candidates, while MCDM was employed to estimate the performance for a job placement. The results show the proposed system provides a qualified matching according to the results collected from enterprise managers.en_US
dc.language.isoen_USen_US
dc.subjectSupport vector machineen_US
dc.subjectTOPSISen_US
dc.subjectFive-factor personality inventoryen_US
dc.subjectCandidate recruitingen_US
dc.subjectPersonality traiten_US
dc.titleBuilding a qualitative recruitment system via SVM with MCDM approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10489-009-0204-9en_US
dc.identifier.journalAPPLIED INTELLIGENCEen_US
dc.citation.volume35en_US
dc.citation.issue1en_US
dc.citation.spage75en_US
dc.citation.epage88en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000291738400006-
dc.citation.woscount1-
Appears in Collections:Articles


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