A Hybrid Machine Learning and Natural Language Processing Approach for Career Recommendation Systems
In today’s competitive job market, students and job seekers often find it hard to go through the huge number of available job opportunities. This can lead to “choice paralysis” and mismatched applications. This paper suggests an automated Job and Internship Recommendation System meant to connect user’s skills with the job that they are looking for or the job that the user would love. It analyzes key traits such as technical skills, talents of the user, academic background, and past experience from user resumes, matching them with a carefully selected database of job descriptions through Content-Based Filtering. To improve accuracy, the system also includes Collaborative Filtering, which recommends roles based on the preferences of users with similar profiles. The experimental results show that this model greatly cuts down the time spent on manual searches. It also improves the relevance of recommendations compared to traditional keyword-based platforms. By offering a streamlined, datadriven way to explore career options, this paper serves as a useful tool for making recruitment more efficient and helping candidates find positions that best fit their career goals and to find the right job that the candidate is searching for so that they can not only earn for a living but to also enjoy what they do which is the main goal of this paper. Thus, the users can easily maintain their profiles, update their skills, and access personalized, real-time job and internship recommendations successfully.