• Harsh Oza
• Darrel Noronha
• Shelton Jade Pinto
• Janhavi Parab
• Prof. Dakshata Panchal

The problem of shortlisting and screening of candi- dates to find the best match in given time frame has always been a challenging task for recruiters in emerging as well as established companies. The paper proposes an intelligent system that could help in easing the recruitment process by mapping the resumes of the best candidates to the company’s job description. The described algorithm is based on ranking candidates on the basis of a scoring system that will act as a mapper. Ranking of the candidates would be on the basis of certain parameters which will be extracted from the resume parser using Machine Learning(ML) and Natural Language Processing(NLP) Techniques which are the best match with the requirements in a specified job description. Also, recommending resumes to the company based on the previous selection of candidates for a similar job description using Recommender Systems is a part of such an intelligent system. The accuracy of the system was calculated by using Normalized Discounted Cumulative Gain accuracy metric. The significance of this project is to bridge the gap and the challenges faced in the recruitment process.

Resume Recommendation, Resume Similarity, Re- sume Ranking, Natural Language Processing, Machine Learning, Online Recruitment

"Company To Candidate Mapper", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.189-193, October-2021, Available :http://www.jetir.org/papers/JETIRFD06027.pdf

"Company To Candidate Mapper", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. pp189-193, October-2021, Available at : http://www.jetir.org/papers/JETIRFD06027.pdf