Recent Advances in Machine Learning Algorithms for Candidate–Job Matching

Citation: Roman Ishchenko, "Recent Advances in Machine Learning Algorithms for Candidate–Job Matching", Universal Library of Engineering Technology, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The candidate–job matching (CJM) problem, central to high-skill recruitment in domains like technology, management, and finance, has seen rapid progress through machine learning (ML) since 2021. Modern systems move beyond simple keyword matching, leveraging advanced natural language processing (NLP), graph representations, and hybrid recommender methods. Transformer-based models (e.g. BERT and derivatives) now embed resumes and job descriptions into semantic spaces, enabling nuanced similarity comparisons. Graph neural networks (GNNs) capture rich relationships among candidates, skills, and jobs, often outperforming traditional neural models in screening tasks. Classical ML approaches (e.g. support vector machines, tree ensembles) remain useful for structured feature matching but are complemented by deep models for unstructured text. Recommender-system techniques – including collaborative filtering, content-based filtering, and hybrid designs – incorporate contextual signals (experience, industries, user behaviors) to improve personalization. Reviewed benchmarks report that fine-tuned transformers and GNNs can significantly boost ranking accuracy (e.g. ~15% NDCG improvements [1]) and screening sensitivity (e.g. GNN balanced accuracy 65.4% vs 55.0% for a plain MLP [2]). These gains come with challenges: neural approaches often act as black boxes, raising interpretability concerns, and large models incur high computational costs that demand scalable architectures (e.g. bi-encoder retrieval with cross-encoder re-ranking in multi-stage pipelines). Bias mitigation has become critical; domain-specific models have been shown to yield fairer outcomes than off-the-shelf large language models. This review surveys recent (2021–2025) peer-reviewed work on CJM, covering algorithmic approaches (SVMs, ensemble trees, Siamese and cross-encoder transformers, GNNs, and hybrid recommenders), model architectures, input representations (resumes, job text, skill ontologies), and evaluation methods. We synthesize experimental findings from academic studies, discussing strengths and limitations of each approach, including accuracy, robustness, interpretability, and fairness. Finally, we highlight open challenges and directions for making CJM more transparent and equitable while maintaining scalability in practice.

Keywords: Candidate–Job Matching; Graph Neural Networks; Machine Learning; Recommendation Systems; Transformer Models.

https://doi.org/10.70315/uloap.ulete.2025.0204004