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Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
Open accessAbstract
In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.
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“Regarding gender equality (SDG 5); predictive analytics have enabled people analytics teams to effectively enhance their recruitment practices; becoming more diverse and inclusive when screening and selecting candidates (Pessach et al., 2020).”
From Reframing people analytics value creation: Balancing organizational performance and common good HRM values (2026) · cites this paper as Pessach et al., 2020
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“Pessach et al. (2020) demonstrated a model of machine learning with mathematical programming for prediction of employee placement to improve the recruitment and placement process and its success rate.”
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Ethical AI in HR: A Case Study of Tech Hiring
Rao, S., & Zhao, T. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2446954 · Cited by 20
Arora, M., Gupta, J., Mittal, A., & Prakash, A. (2024) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-04-2024-0237 · Cited by 23
Artificial Intelligence in Human Resource Management: A PRISMA-based Systematic Review
Benabou, A., & Touhami, F. (2025) · Acta Informatica Pragensia · doi:10.18267/j.aip.264 · Cited by 27
Hüllermeier, E., & Słowiński, R. (2024) · 4OR · doi:10.1007/s10288-023-00561-5 · Cited by 14
“…decision-making algorithms trained on data can already be found in various domains, such as hiring or placement decisions in employee recruitment (Pessach et al. 2020), the construction of individualized treatment rules in personalized medicine (Zhao et al. 2012), disaster management (Zagorecki…”
Artar, M., Balcıoğlu, Y. S., & Erdil, O. (2024) · Management Decision · doi:10.1108/md-12-2023-2295 · Cited by 22
Revillod, G. (2025) · Systems Research and Behavioral Science · doi:10.1002/sres.3140 · Cited by 18
BIBLIOMETRIC ANALYSIS OF HR ANALYTICS LITERATURE
Allaham, M. V. (2022) · Elektronik Sosyal Bilimler Dergisi · doi:10.17755/esosder.950426 · Cited by 17
A Machine Learning-Based AI Framework to Optimize the Recruitment Screening Process
Ujlayan, A., Bhattacharya, S., & Sonakshi (2023) · International Journal of Global Business and Competitiveness · doi:10.1007/s42943-023-00086-y · Cited by 22
“In another study, Pessach et al., (2020) proposed a prescriptive analytics approach combining machine learning and mathematical programming for employee recruitment.”
Rabkin, L., Cohen, I., & Singer, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107914 · Cited by 13
Self-citationComparing expert systems and their explainability through similarity
Gwinner, F., Tomitza, C., & Winkelmann, A. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114248 · Cited by 14
Feng, Y., Yin, Y., Wang, D., Ignatius, J., Cheng, T. C. E., Marra, M., & Guo, Y. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114872 · Cited by 13
Leveraging AI for the Reinforcement of GHRM
Anshima, A., & Bhardwaj, B. (2023) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-0418-1.ch005 · Cited by 13
Artificial intelligent technologies in Japanese manufacturing firms: an empirical survey study
Hadid, W., Horii, S., & Yokota, A. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2358409 · Cited by 13
“…function, sales function (Paschen, Wilson, and Ferreira 2020), marketing function (Campbell et al. 2020), human resource management function (Pessach et al. 2020), purchasing function (Allal-Chérif, Simón-Moya, and Ballester 2021), research and development function (Keding and Meissner…”
A flexible employee recruitment and compensation model: A bi-level optimization approach
Gal, H. C., Forma, I. A., & Singer, G. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107916 · Cited by 17
“Our proposed tool allows management to incorporate valuable knowledge and experience into the model, and it yields actionable solutions (Pessach et al., 2020).”
MethodSelf-citationLin, L.-S., Lin, Y.-S., Li, D., & Liu, Y.-H. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.113996 · Cited by 12
Lumbantoruan, J. H., & Manalu, R. U. (2023) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v13i1.25354 · Cited by 15
“Dominant in society, making mathematics is a benchmark for the success of students in school [2].”
BackgroundWhy Do Swiss HR Departments Dislike Algorithms in Their Recruitment Process? An Empirical Analysis
Revillod, G. (2024) · Administrative Sciences · doi:10.3390/admsci14100253 · Cited by 17
The Use of Artificial Intelligence ( AI ) in Talent Acquisition: The Case of Greek Luxury Hotels
Marinakou, E., Giousmpasoglou, C., & Papavasileiou, E. F. (2024) · Strategic Change · doi:10.1002/jsc.2632 · Cited by 11
Metadata
- Authors
- Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I.
- Affiliations
- Tel Aviv University (IL)
- Bar-Ilan University (IL)
- Corresponding author
- Pessach, Dana
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 134
- Pages
- 113290
- ISSN
- 0167-9236 (ISSN-L)
- PMC
- PMC7252110
- OpenAlex
- W3014109009
- Cited by
- 304
- References
- 61
- Citations by year
- 2026: 46 · 2025: 62 · 2024: 74 · 2023: 57 · 2022: 32 · 2021: 26 · 2020: 7
- FWCI
- 32.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Decision Support Systems (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Koret Foundation
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- personnel selection, hiring decisions, model interpretability, prescriptive analytics, recruitment optimization, diversity in hiring
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