A COMPREHENSIVE REVIEW OF APPROACHES, CHALLENGES IN CAREER RECOMMENDATION SYSTEMS

Abstract

Abstract. This research presents an extensive investigation into recommendation systems pertinent to career guidance, encompassing job matching, education, and skill development applications. The study rigorously examines methodologies, algorithms, and data sources integral to these systems, evaluating their strengths and limitations. It thoroughly explores evaluation metrics, real-world case studies, and emerging trends, emphasizing challenges like data sparsity, scalability, and fairness.
Furthermore, the paper provides a comprehensive analysis of machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms within recommender systems. By illuminating their strengths, applications, and constraints, the study highlights the intricate interplay of these algorithms within recommendation systems. It addresses challenges including cold-start issues, the stability-plasticity balance, and user satisfaction, offering insights into navigating these complexities.
This research serves as an indispensable guide for researchers and practitioners alike, providing comprehensive insights into machine learning, deep learning, and reinforcement learning algorithms' roles within career recommendation systems. It underscores the significance of overcoming inherent limitations and advocates for innovative solutions to enhance these systems' effectiveness and applicability in real-world scenarios.
Keywords: collaborative filtering, content-based filtering, hybrid-based recommendation systems, k-nearest neighbors, decision trees, random forests, reinforcement learning, deep neural networks, convolutional neural networks.