IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Resume Extractor and Candidate Recruitment System using Online Test and SMTP


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International Journal for Innovative Research in Science & Technology
Volume 5 Issue - 9
Year of Publication : 2019
Authors : Dr. S F Sayyad ; Shrushti Bhingardive; Prashant Rajendran; Preeti Ghatnekar; Kajal More

BibTeX:

@article{IJIRSTV5I9013,
     title={Resume Extractor and Candidate Recruitment System using Online Test and SMTP},
     author={Dr. S F Sayyad, Shrushti Bhingardive, Prashant Rajendran, Preeti Ghatnekar and Kajal More},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={5},
     number={9},
     pages={9--11},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV5I9013.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

Automated Resume Extraction and Candidate Selection System is a product which can be best suited for any organization’s recruitment process. The system will be robust enough which will automatically extract the resume content and store it in a structure form within the Data Base [1]. Classification algorithm (Naive Bayes) will be run on the profiles to identify profile Categories or classes. Also the employer can specify his criteria and also decide the importance level. As the internet grows, amount of electronic text increases rapidly. This brings the advantage of reaching the information sources in a cheap and quick way. Keywords are useful tools as they give the shortest summary of the document. But they are rarely included in the texts [2]. There are proposed methods for automated keyword extraction. This paper also introduces such a method, which identifies the keywords with their frequencies and positions in the training set. It uses Naïve Bayesian Classifier with supervised learning.


Keywords:

Conceptual Matching; Resume Extraction; Online Recruitment; Knowledge base Assisted Classification


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