Please use this identifier to cite or link to this item:
http://repository.kln.ac.lk/handle/123456789/15614
Title: | Intelligent Recruitment Management Engine |
Authors: | Chiththananda, H.K.I.C.L. Perera, T.D. Rathnayake, L.M. Mahanama, M.G.G.D.D.P. Prabashana, P.M.P. Dias, D.P.N.P. |
Keywords: | information extraction information optimization ontology prediction |
Issue Date: | 2016 |
Publisher: | Faculty of Computing and Technology, University of Kelaniya, Sri Lanka |
Citation: | Chiththananda, H.K.I.C.L., Perera, T.D., Rathnayake, L.M., Mahanama, M.G.G.D.D.P., Prabashana, P.M.P., and Dias, D.P.N.P. 2016. Intelligent Recruitment Management Engine. Kelaniya International Conference on Advances in Computing and Technology (KICACT - 2016), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka. p 10. |
Abstract: | A system which can be used to automate the recruitment process of an organization is proposed in this paper. The system is designed targeting the human resource department of an organization in order to simplify the massive process of data extraction from a large number of curriculum vitaes (CVs), on the other hand to reduce the cost and time which have to be spent on the interview process and to allocate most suitable interviewers from the organization for each interviews by analyzing the past data. Information extraction is used in-order to retrieve data from CVs as well as from the cover letters. An ontology map is created to analyze and categorized the extracted key words through this system. Then the CVs are sorted and prioritized according to the requirements of the organization. We come up with a prediction component which will be embedded in the main system to predict the future of the incoming values such as the cost and time of the recruitment process of a particular organization by analyzing the past records. Hence we believe that this system will enhance the efficiency and effectiveness of the recruitment process of any organization. |
URI: | http://repository.kln.ac.lk/handle/123456789/15614 |
ISBN: | 978-955-704-013-4 |
Appears in Collections: | KICACT 2016 |
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