Job Recommendation System: Content-Based and Collaborative Filtering for Predictive Job Recommendation Systems

Abstract

The Smart work Recommendation System (SWR) was created to meet the difficulties presented by the complex work market of today, which is being impacted by design and the recession. To provide highly customized job recommendations, the SJR uses a hybrid technique that combines the advantages of collaborative filtering (CF) and content-based filtering (CBF). CBF evaluates user talents using Natural Language Processing (NLP) and compares them to pertinent job descriptions. Concurrently, CF finds appropriate job suggestions by analyzing the application history and interests of comparable individuals.By adding features for pay prediction and behavioral profile, the SJR goes above and beyond conventional methods. To ensure a strong fit that goes beyond skills, behavioral profiling examines user behavior and preferences to find team dynamics and company cultures that mesh well. Real-time pay insights are provided via the integrated salary prediction tool, enabling recruiters and job seekers to make well-informed judgments about salary expectations and negotiations.The SJR uses machine learning algorithms to examine patterns and provide pertinent recommendations based on similar user profiles, so addressing the cold start issue that new users face. In order to provide consistently accurate and tailored recommendations for every user, the system is built to continuously learn from and adjust to changing user preferences and employment market trends. The SJR provides a much improved job search experience by integrating behavioral analysis, pay projection, and sophisticated filtering techniques, giving recruiters and job seekers a more accurate, individualized, and effective platform.