Smart Job Matcher Enhancing Employment Search with ML Algorithms

Abstract

The rapid growth of artificial intelligence and machine learning technologies has led to significant advancements in recommendation systems that aim to match users with relevant content. This paper reviews key techniques used in job recommendation systems, including content-based filtering and collaborative filtering, and explores algorithms like TF-IDF (Term Frequency-Inverse Document Frequency), cosine similarity, and linear kernel. These methods are evaluated for their efficiency in matching job seekers with suitable jobs based on both job content and user profiles. The TF-IDF Vectorizer is employed to transform textual job descriptions and user profiles into numerical vectors, while cosine similarity measures the similarity between job listings and user profiles. The linear kernel offers computational efficiency in similarity calculations. Additionally, collaborative filtering recommends jobs to users by identifying similar users based on their profiles and previous applications. The study also includes exploratory data analysis (EDA) for data preprocessing and visualization to highlight job distribution trends. The paper compares the accuracy of these models, finding that content-based filtering techniques achieve high precision (~80%) in job similarity matching, whereas collaborative filtering has slightly lower accuracy (~70%) due to the cold-start problem for new users. The paper concludes with recommendations for developing more effective job recommendation systems, incorporating career move suggestions such as upskilling and reskilling for future research.