Vritthi - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles
Animesh Giri, Abhiram Ravikumar, Sneha R. Mote, Rahul Bharadwaj · 2016
In this model, we propose an innovative recruitment system using social networking websites like Twitter and LinkedIn along with code repository hosting website GitHub and competitive coding platforms like SPOJ. It is aimed to develop advanced search engines to automatically sort the job-seekers based on job offer requirements using various data mining and machine learning techniques. Vritthi allows job-seekers to quantify their job preparedness and offer a list of specific areas for them to focus on. We propose the formulation of VPQF (Vritthi Professional Quotient) that involves the use of K-means algorithm to classify users into appropriate clusters and provide them with appropriate suggestions for improvement. Using classic data mining techniques like filtration, classification, clustering, profiling as well as string matching & user profiling, this tool will enable recruiters to effectively select candidates who fit their organization in a hassle-free automated manner.