Machine Learning Methods for Solving Complex Ranking and Sorting Issues in Human Resourcing

Abstract

Every organization doesn't necessary to have the common point of view of a particular resume while considering for a job description (JD). Keeping the same role in place, while some stress on technical skills, the other give importance to professional experience and domain expertise. Understanding these hiring patterns are becoming important in today's head hunting. The traditional job search engines offers resumes which matches to the input keywords. As the search outcomes from these search engines grows, the problem in selecting the best profile surges. The role of Human Resource (HR) staff becomes more important in understanding these hiring patterns and suggesting the suitable profiles. HR staff proposes these profiles which are ranked manually. The proposed method is to understand the intelligence behind the hiring pattern and apply the machine learning to accommodate the identified intelligence. The proposed method offers the ranking system according to the hiring patterns. Highly trained models along with the traditional search method, predicts the ranking and sorting of resumes with high accuracy and simplifies the job of human resourcing efficiently.