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Automated Filtering of Relevant Resumes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 154 - Number 6 |
| Year of Publication: 2016 |
| Authors: Prarthita Das, Amala Deshpande |
10.5120/ijca2016912162
|
Prarthita Das, Amala Deshpande . Automated Filtering of Relevant Resumes. International Journal of Computer Applications. 154, 6 ( Nov 2016), 34-36. DOI=10.5120/ijca2016912162
Abstract
In today’s world, there is a colossal rise in the number of job seekers. Additionally, with increasing applicants the number of resumes increases proportionally, thereby making the task of the HR department extremely laborious. Therefore, a new approach has been proposed to automate the task of recruiting suitable candidates for a given job profile by matching the candidate’s qualities to the parameters mentioned by the recruiter which involves parsing the resume automatically. This system, takes the unstructured or structured resume as input from the applicant and the job specifications from the recruiter (which acts like a query) and then using information extraction, storing and matching techniques a certain relevancy percentage is calculated which determines the extent to which the candidate is suitable for that post. The higher the percentage, the better the candidate is for that portfolio, thereby providing the recruiter with the best results for that given job profile. Therefore, this proposed system makes the task of recruiting more efficient and faster, and also eliminates the need to manually find the best suited applicants.
References
- Sunil Kumar Kopparapu, “Automatic Extraction of Usable Information from Unstructured Resumes to Aid Search”, published in IEEE 2010
- V. jayaraj, V. Mahalakshmi, P. Rajadurai, “Resume Information Extraction using Feature Extraction Model”, published in AIJRSTEM 2015
- V. Jayaraj and P. Rajadurai, “Information extraction using clustering of resume entities,” published in 01 January 2016 publication in International Journal of Science Technology and Management.
- Haitao Xiong and Junjie Wu Lu Liu, “Classification with class overlapping: A systematic study,” in 2010 International Conference on E-business Intelligence.
- Chanawee Chanavaltada, Panpaporn Likitphanitkul, Manop Phankokkraud, "An Improvement of Recommender System to Find Appropriate Candidate for Recruitment with Collaborative Filtering", published in 2015 ICCSS
- Dr Lakshmi Rajamani, Mohd Mahmood Ali, "Automation of decision making process for selection of talented manpower considering risk factor: A Data Mining Approach", published in IEEE 2012
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