AI based suitability measurement and prediction between job description and job seeker profiles

Hiring a suitable candidate for a certain job is highly demanding and requires several intense processes. Many organizations face challenges to hire a suitable candidate as they seek specific requirements mentioned in the Job Description (JD). An Artificial Intelligence (AI) based system is developed to measure and predict a suitable candidate from an available Candidate Resume (CR) database. Four clusters are prepared from JD and CR corresponding to primary skills, secondary skills, adjectives, and adverbs. The Jaccard similarity is measured between these clusters and a suitability measure is proposed based on the cluster parameters. Using the three classifiers linear regression, decision tree, Adaboost, and XGBoost the prediction of candidate suitability is performed. To carry out the classification tasks various features are formed by employing the bag of words technique. The maximum average accuracy of 95.14% is achieved for the XGBoost classifier.