Hybrid Employment and Entrepreneurship Recommendation System based on K-Nearest Neighbors

The contemporary labor market needs sophisticated employer and job-seeker profiling. A Hybrid Employment and Entrepreneurship Recommendation System, combining collaborative filtering, content-based analysis, and knowledge-based rules, is proposed. While AI-based recommendations are more efficient and scalable, efforts to address algorithmic bias, data privacy, and user-oriented design need to be made. The system uses K-Nearest Neighbors (KNN) for peer suggestions, Logistic Regression for matching skills to jobs, and a rule-based engine to impose restrictions such as location and salary. With an accuracy of 97.6%, precision of 96.6%, recall of 95.3%, and 97.2% F1-score, the system surpasses conventional approaches by finding the right balance between relevance and diversity—mapping chances to qualifications, desires, and real-world constraints. Apart from technical ingenuity, the system focuses on flexibility (through user input) and explainability, with clear rationale for suggestions. This strategy converts disorganized job searches into organized, opportunity-focused journeys, acting as a wise career counsellor instead of a mere algorithmic program.