RezFind

Author:

K. Srikanth1,Mishra Dibya Nandan1ORCID,Pujari Pratyusha1,Sravya Pingili1,K. Vishal1,Chenna Sankalp1

Affiliation:

1. Woxsen University, India

Abstract

A typical job posting on any job-hunting portal like Linkedin, Naukri, Indeed, etc., will receive many resumes. Screening a resume manually is a tedious process involving huge costs. Screening resumes also consumes a lot of time for the hiring managers. Sometimes, because of the massive numbers, a few qualified resumes don't get noticed, leading to considerable loss to both the company and a loss of opportunity for the applicant. This study uses advanced natural language processing to automate the resume screening process. It also describes a data mining method to extract relevant information like the eligible applicant's name, contact, and email. RezFind provides a unique scoring system that gives a similarity score between the job description and the resume to keep it very specific to the job posting instead of a generic screening. This process allows keeping the uniqueness of each job role, and the screening quality increases by having a specific job description.

Publisher

IGI Global

Reference18 articles.

1. E-recruiting support system based on text mining methods

2. AllahyariM.PouriyehS.AssefiM.SafaeiS.TrippeE.GutierrezJ.KochutK. (2017). A Brief Survey of Text Mining: Classification. Clustering and Extraction Techniques.

3. Amro, B., Najjar, A., & Macido, M. (2022). An Intelligent Decision Support System For Recruitment: Resumes Screening and Applicants Ranking. http://dspace.hebron.edu:80/xmlui/handle/123456789/1124

4. Resume Screening using NLP and LSTM

5. A Two-Step Resume Information Extraction Algorithm

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