Reducing the complexity of candidate selection using Natural Language Processing

The recruitment industry is facing major challenges in terms of selecting candidates who best fit a vacancy. Artificial Intelligence, Machine Learning, and Natural Language Processing, although they have greatly increased the processing of mass data for candidates, are not without limitations. This paper presents a hybrid solution to candidate selection with a job title that is fitting for an open vacancy. This hybrid approach is a combination of two Machine Learning techniques, XGBoost and BERTopic. This solution reduces processing time and memory usage. The definition of job title similarity in the context of similar industries provides higher accuracy to the complex and delicate process such as candidate selection fitting for an open vacancy.