Job Recommendation System using LinkedIn User Profiles

The rapid expansion of professional networking platforms has created vast opportunities for personalized career guidance. LinkedIn provides rich user data including skills, education, work experience, endorsements, and professional interests. This project proposes the design and implementation of a Job Recommendation System that leverages LinkedIn user profiles to suggest relevant career opportunities. The system integrates Natural Language Processing (NLP) and machine learning algorithms to analyze user attributes such as skills, qualifications, and professional history. By employing collaborative filtering and content-based recommendation techniques, the framework matches user profiles with job postings from multiple sources. The proposed system reduces the time and effort required by job seekers in identifying suitable opportunities while assisting recruiters in targeting the right candidates. Experimental evaluation demonstrates that the recommendation system improves accuracy and relevance compared to traditional keyword-based search methods, thereby enhancing the overall job search experience