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Authors: Dinesh, P.A.D.K
De Silva, D.M.
Jayaweera, K.S.S
Gnanawardena, H.G.J.H.
Mallawarachchie, Yashas
Keywords: Machine Learning Algorithms
Job matching
Three Factor model
Sentiment analysis
Issue Date: 2017
Publisher: SLIIT
Series/Report no.: ;17-108
Abstract: With the advancement of the technology, most of the work become automated. While Internet takes up by far the most significant part of our daily lives, finding jobs/employees on the Internet has started to play a crucial role for job seekers and employers. The primary use of online job ads traditionally has been to connect job seekers with available openings. Yet many students across the world have recently graduated from college and are looking for jobs. In a market of heterogeneous jobs and job seekers that has imperfect and asymmetric information, matching the right job to the right candidate is very difficult. In this study, an interactive job searching and matching system is designed and a prototype is implemented. The proposed system enables the employer to select the right candidate quicker and decreases the possibility of hiring the poor performing individuals. The system predicts the ideal career path for job seekers and generates a market value which will help them to market themselves for a better job. The system facilitates the pre-selection of candidates for employment and it suggests suitable job vacancies for job seekers. A personality prediction method has also been implemented. A supervised machine learning algorithm named Naïve-Bayes is used as the basis and all technologies used are free and open source.
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