AI-Driven Professional Profile Categorization and Recommendation System

Abstract

The exponential growth of applications in digital and information system domains has made the identification of qualified candidates increasingly complex, resulting in longer and less efficient recruitment processes. Recruiters frequently deal with heterogeneous and unstructured résumés, which complicates skill assessment and increases the risk of mismatches between candidates and job requirements. To address these challenges, this research proposes an AI-based framework for the automatic classification and recommendation of professional profiles using natural language processing (NLP), text mining, and supervised machine learning techniques. The methodology includes the comparative evaluation of several classification algorithms—Logistic Regression, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Gradient Boosting (GB), and Naïve Bayes—to identify the most accurate and robust model. The framework also incorporates a similarity-based matching mechanism to align candidate profiles with job postings. Experimental results show a classification accuracy of 96.38%, demonstrating the model’s effectiveness in enabling faster, more reliable, and objective recruitment decisions while providing candidates with insights into their compatibility with labor market expectations.