resume recommendation system using natural language processing
Resume, Machine learning, NLTK, Cosine similarity, Decision Tree, Logistic Regression, Resume matching, similarity measure, Content based filtering.
A company’s progress gets slowed down if a wrong person gets recruited for the job position. It is a tedious task to find a suitable candidate for an open position when there are many candidates. This research paper showcases about the different recommendation techniques which can make the recruitment process easier and faster. Classification is done as per the job description, which then shows the percentage match of the resume with respect to the job description. Only if there is a 50 % match between the resume and the job description can the recruitment process move any further. Domain knowledge is required for screening of resumes. India is big job market where millions of people seek jobs and it is a tough task to separate the right candidate from this huge market. And so, hiring costs many resources to the company. Resumes do not have a standard format; every resume has a different format and a different structure. The recruitment team then has to manually match each resume with respect to the job description. Manual process has a high chance of missing the right candidate for the job within the process. The classification techniques here come to play and make it easier for the company as well as the candidate it will add value to the company’s recruitment drives, these techniques are being researched to bring a more hassle-free experience to the recruitment process.
"resume recommendation system using natural language processing", IJEDR - INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (www.IJEDR.org), ISSN:2321-9939, Vol.10, Issue 2, page no.59-64, May 2022, Available :https://rjwave.org/IJEDR/papers/IJEDR2202010.pdf
Volume 10
Issue 2,
April-2022
Pages : 59-64
Paper Reg. ID: IJEDR_220060
Published Paper Id: IJEDR2202010
Research Area: Engineering
Country: lucknow, uttar pradesh, India
ISSN: 2321-9939 | IMPACT FACTOR: 9.37 Calculated By Google Scholar | ESTD YEAR: 2013
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.37 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJEDR (IJ Publication) Janvi Wave
