A Survey: Profile Evaluation and Job Suggestion using TNN

This study explores the application of a two-layer neural network (TNN) for automated profile evaluation and job suggestion. With the increasing demand for personalized job recommendations, traditional methods often fall short in handling the complex, non-linear relationships between an individual's skill set and job requirements. The TNN approach aims to bridge this gap by training on datasets that include user profiles, educational backgrounds, skill sets, work experiences, and job descriptions. The network's first layer focuses on feature extraction from user profiles to determine their core competencies, while the second layer maps these competencies to relevant job roles in the market. By employing supervised learning techniques, the TNN continuously improves its predictive accuracy based on feedback from real-world outcomes. The results indicate that the TNN model can significantly enhance job-matching accuracy, providing tailored job suggestions that align closely with an individual's qualifications and career aspirations. This approach holds potential for use in recruitment platforms and career development tools, offering a scalable solution for personalized career guidance.