Enhancing Job Matching Through Natural Language Processing: A BERT-based Approach

With the rise of web-based job sites, job-seeking has undergone a significant transformation, making it easier to find suitable opportunities. This study proposes a solution using machine learning and natural language processing to enhance job recommendations. The dataset used is free from prior user interactions, ensuring fairness in the system’s recommendations. The system combines collaborative and content-based filtering methods to develop candidate recommendation algorithms. Its performance is evaluated using precision, recall, and F1 score metrics. This approach helps job seekers streamline their job search, saving time and effort. Additionally, improving the chances of finding a job that closely matches their qualifications, increases their likelihood of success in the highly competitive job market.