Job Recommendation Systems Through AI and Machine Learning

The explosive rise in digital recruitment platforms has generated the demand for smart systems that can make effective, personalized job recommendations. This paper introduces a new hybrid AI-driven job recommendation architecture that combines deep learning models (e.g., BERT for resume/job description embedding) with knowledge graph reasoning (to incorporate domain-specific semantics and relations) and reinforcement learning (for ongoing personalization). The system is tested against real-world job market data sets (e.g., Kaggle data set, Indeed, LinkedIn samples) and compared to baseline collaborative and content-based filtering methods. The outcomes show remarkable enhancements in recall, precision, and user satisfaction, which speak to the merits of fusing semantic comprehension with adaptive learning..