A COMPREHENSIVE REVIEW OF APPROACHES, CHALLENGESIN CAREER RECOMMENDATION SYSTEMS
DOI:
https://doi.org/10.47344/sdubnts.v64i1.1148Keywords:
collaborative filtering, content-based filtering, hybrid-based recommendation systems, k-nearest neighbors, decision trees, random forests, reinforcement learning, deep neural networks, convolutional neural networksAbstract
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.
