BERT-based Job Recommendation System Using LinkedIn Dataset

Abstract

Amidst rapid technological progress, bridging the gap between job seekers and employers has become increasingly important as the job market evolves. This research presents a job recommendation system that leverages the Bidirectional Encoder Representations from Transformers (BERT) model, a powerful Natural Language Processing (NLP) framework. The system ensures precise and personalized recommendations by understanding the semantic relationships within job descriptions and user profiles. The model integrates contextual matching of skills and preferences, addressing the limitations of traditional content-based methods. Evaluation results demonstrate effectiveness in recommendation accuracy. This research proposes an alternative approach to the job-matching process by harnessing BERT’s bidirectional contextual capabilities. The presented work has significant implications for human resources platforms, job portals, and recruitment solutions in an increasingly digital workforce ecosystem.