Novel Hiring Process using Machine Learning and Natural Language Processing
Neha Vaishnavi Sharma, Rigzen Bhutia, Vandana Sudhakar Sardar, Abraham P. George, Farhan Ahmed · 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · 2021
There is an ever-increasing number of students graduating from colleges every year and entering the workforce. With their primary means of securing employment being through campus hiring and online portals, and the hiring process being automated to a large extent with more and more employers willing to using use automation and other processes such as psychometric tests for job judging suitability of a candidate, it leads to the candidate feeling overwhelmed and confused regarding job applications and the employers may not be not be able to get an accurate assessment during the hiring process. This work seeks to apply domain classification on user uploaded resumes and map user profiles into distinct clusters based on their performance on an aptitude/technical test. The user will be provided with hybrid recommendations based on association rule mining. This proposed hiring process aims to eliminate employee attrition and newly passed out students can get employment based on their interests and abilities.