Method for Job Recommendation based on Machine Learning and Deep Learning Model

In the modern digital era, the integration of machine learning (ML) and deep learning (DL) in job recommendation systems offers a transformative approach to bridging candidates with appropriate job opportunities. Traditional job matching techniques, reliant on human evaluation, are often fraught with inefficiencies and inaccuracies, especially given the extensive volume of job seekers and openings. This study introduces an innovative job recommendation methodology that synergistically combines the capabilities of ML and DL models. Our proposed model leverages the adaptability of ML algorithms to process diverse datasets and the power of DL neural networks to discern intricate patterns within vast data troves. Initial findings demonstrate an improved recommendation accuracy and a reduction in the “cold start” problem often encountered in recommendation systems. Furthermore, the study addresses fairness concerns, ensuring that proposed model provides equitable suggestions across various demographics. By integrating these cutting-edge technologies, our method not only enhances the job-seeking experience for candidates but also streamlines recruitment processes for employers. The overarching aim is to foster a more efficient, transparent, and inclusive job market.