vRecruit: An Automated Smart Recruitment Webapp using Machine Learning

Abstract

The need for global online recruitment has risen tremendously in recent years. However, this procedure presents difficulties for recruiters in managing the flood of applications and maintaining contact with the applicants. Historically, little attention has been paid to a practical solution for virtual recruitment. As a result, the paper proposes "vRecruit - A machine learning-based web application" for virtual recruitment in the current paper. vRecruit’s primary features include a client-specific interview process that leverages Machine Learning-based references to context provided by the client, as well as a text-based sentiment analysis engine. All components work in unison to ensure the webapp’s end-to-end functionality, which was finally launched on flask. The face recognition method using the face api model achieved a 96% accuracy. The speech to text conversion using the Mozilla DeepSpeech model had a 7.55% word error rate, whereas the rasa Natural Language Understanding (NLU) model trained for chatbots had a 95% accuracy. The webapp provides a hassle-free virtual recruiting experience for candidates and interviewers.