★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★Open research. Global perspectives.
editor@theamericanjournals.comPublisher Home ↗
Articles OPEN ACCESS

Evolution of Applicant Tracking Systems: From Databases to Intelligent Platforms

Mykhailo Petrenko
AI Agents Engineer at Apple Austin, USA
DOI Crossref DOI 10.37547/tajiir/Volume08Issue01-10
tajiir 2026
VOL. 8 / NO. 01 JANUARY
FULL TEXT PP. 63-71
DOWNLOAD PDF

View all articles in this issue ↗

VOLUME 8
ISSUE 01
YEAR 2026
PAGES 63-71
RESEARCH OVERVIEW

Abstract

The paper examines the transition of applicant tracking systems (ATS) from record-keeping databases to intelligent decision-support platforms grounded in representation learning and modular architectures. The study synthesizes peer-reviewed findings on semantic resume–job matching, learning-to-rank pipelines, human-in-the-loop re-ranking, and governance practices for fairness and auditability. Particular attention is paid to latency-aware MLOps, API-first interoperability, and explanation surfaces that restore recruiter control while compressing screening cycles. The analysis aligns these capabilities with persistent U.S. hiring frictions—screening workload, time-to-shortlist, and cost-per-hire—showing where embedding-based triage and event-driven integration yield measurable improvements in throughput and shortlist quality. The article proposes a product blueprint: fidelity-preserving parsing, domain-tuned encoders, hybrid re-rankers, continuous bias and drift monitoring, and evented integration with enterprise HR stacks. The discussion outlines risk–control mappings (bias, drift, opacity, load) and operational metrics for evaluation. Findings inform platform designers, HR leaders, and policy stakeholders seeking accountable automation that reduces delay while improving match quality.

RESEARCH TOPICS

Keywords

applicant tracking systems talent acquisition resume–job matching sentence embeddings learning-to-rank explainability bias mitigation MLOps interoperability human-in-the-loop

Downloads

Download data is not yet available.
SOURCES & SCHOLARSHIP

References

  1. Alonso, R., Dessí, D., Meloni, A., & Recupero, D. R. (2025). A novel approach for job matching and skill recommendation using transformers and the O*NET database. Big Data Research, 39, 100509. https://doi.org/10.1016/j.bdr.2025.100509
  2. Bevara, R. V. K., Mannuru, N. R., Karedla, S. P., Lund, B., Xiao, T., Pasem, H., Dronavalli, S. C., & Rupeshkumar, S. (2025). Resume2Vec: Transforming applicant tracking systems with intelligent resume embeddings for precise candidate matching. Electronics, 14(4), 794. https://doi.org/10.3390/electronics14040794
  3. Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, 567. https://doi.org/10.1057/s41599-023-02079-x
  4. Li, Y., Liu, C., Liu, L., Masnou, S., & Schönlieb, C. B. (2025). GeoSplat: A deep dive into geometry-constrained Gaussian splatting. arXiv Preprint arXiv:2509.05075. https://arxiv.org/abs/2509.05075
  5. Gartner. (2025). Applicant tracking system (ATS) (glossary entry). Retrieved September 26, 2025, from https://www.gartner.com/en/information-technology/glossary/applicant-tracking-systems-ats
  6. Gheewala, S., Xu, S., & Yeom, S. (2025). In-depth survey: Deep learning in recommender systems—Exploring prediction and ranking models, datasets, feature analysis, and emerging trends. Neural Computing and Applications, 37, 10875–10947. https://doi.org/10.1007/s00521-024-10866-z
  7. Rosenberger, J., Wolfrum, L., Weinzierl, S., Kraus, M., & Zschech, P. (2025). CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations. Expert Systems with Applications, 275, 127043. https://doi.org/10.1016/j.eswa.2025.127043
  8. Society for Human Resource Management. (2025). Recruiting benchmarking report (2025 edition). SHRM. https://www.shrm.org/content/dam/en/shrm/research/2025-recruiting-benchmarking-report.pdf
  9. Chhatre, R., & Singh, S. (2025). Mitigating bias in AI-driven recruitment: Ethical challenges and governance solutions. Journal of Information Systems Engineering and Management, 10(48s). https://doi.org/10.52783/jisem.v10i48s.9566
  10. Deshmukh, A., & Raut, A. (2024). Enhanced resume screening for smart hiring using sentence-bidirectional encoder representations from transformers (S-BERT). International Journal of Advanced Computer Science and Applications, 15(8), 269–278. https://doi.org/10.14569/IJACSA.2024.0150828
  11. Bika, N. (2023). Time to fill and time to hire metrics FAQ. Workable Resources. https://resources.workable.com/tutorial/faq-time-to-fill-hire
  12. Employ. (2024). Empowering people-first recruiting: Employ Recruiter Nation Report 2024. https://nxtthingrpo.com/wp-content/uploads/2025/01/2024-Employ-Recruiter-Nation-Report-Empowering-People-First-Recruiting.pdf
  13. Navarra, K. (n.d.). The real costs of recruitment. Society for Human Resource Management (SHRM). Retrieved October 1, 2025, from https://www.shrm.org/topics-tools/news/talent-acquisition/real-costs-recruitment
  14. Olmstead, L. (2025). Time-to-proficiency: How to accelerate new hire productivity. Whatfix. https://whatfix.com/blog/time-to-proficiency/
  15. Bahr, K. (n.d.). Resume screening. Eddy. Retrieved October 1, 2025, from https://eddy.com/hr-encyclopedia/resume-screening/
  16. Testlify. (2025). Resume screening: What every recruiter should know in 2025. https://testlify.com/resume-screening-every-recruiter-should-know/
  17. Johnson, K. (2016). Recruitment chatbot Mya automates 75% of hiring process. VentureBeat. https://venturebeat.com/business/recruitment-chatbot-mya-automates-75-of-hiring-process