Online Job Search Application with Automatic Recommendation and Notification System: Leveraging AI and ML for Enhanced User Experience

With the continuous advancement of technology and the need to keep pace with the digital era, the implementation of robust automated job recommendation systems has become essential to address the limitations of traditional methods and manual processes. This dissertation focuses on developing an online job search website application that integrates an automatic recommendation, alert and notification system, facilitating a more efficient connection between employers and job applicants using ML. Employers will have the ability to post job openings, review applicant profiles, and select the most qualified candidates. The user profile will be utilized to recommend jobs to candidates through a Semantic-Based Search System, with the Cosine Similarity technique serving as the key factor for automated job recommendations and comparing alongside the behavior of Jaccard similarity and the Jaccard similarity with subset matching. In other to understand what the best scenario for use for each is. The development of this job portal application aims to address the challenges faced by companies in filling vacancies and by job seekers in finding suitable employment opportunities.