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dc.contributor.advisor Chitraranjan, C
dc.contributor.author Selvantharajah, T
dc.date.accessioned 2019-10-16T04:14:23Z
dc.date.available 2019-10-16T04:14:23Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/15062
dc.description.abstract In this research, I propose a robust approach for predicting personality traits of job candidates using machine learning. Relationship between personality traits and job performance has been studied extensively during the past few decades and thus this relationship can be utilized to overcome limitations in choosing the right candidates. The proposed approach uses scenario-based analysis using machine learning techniques. Candidates will be asked to take part in scenario-based written conversations and their personality traits will be extracted from these conversations using machine learning techniques. Exacted personality traits of the candidates will be compared with the required job related characteristics in order to evaluate the fitness for the position for which candidates are applying. In order to categorize personality traits of candidates, the Five Factor model is used. Existing methods of evaluating personality traits such as standard set of questionnaires are susceptible to candidates providing false information and also time consuming. Besides candidates’ qualifications, knowledge and experience, candidates’ personality traits also used to rank the candidates and shortlist them for face-to-face interviews. Thus, this technique not only allows recruiting right candidates to right position but also reduces significant amount of time and cost spent on evaluating candidates’ suitability for given a job position by reducing the number of interviews to conduct. Further, this proposed system can be incorporated into existing e-recruitment system thus leveraging its effectiveness. Therefore, it is beneficial for companies since the proposed system helps to reduce cost and time consumption in the recruitment process while assisting them to choose more suitable candidates for a particular job position. en_US
dc.language.iso en en_US
dc.subject Data Mining en_US
dc.subject Computer Science & Engineering en_US
dc.subject Machine learning en_US
dc.title Recruit best candidates with machine learning en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree M.Sc in Computer science en_US
dc.identifier.department Department of Computer Science & Engineering en_US
dc.date.accept 2018-06
dc.identifier.accno TH3710 en_US


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