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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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Cites
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Wijaya, D. A. P., Jumri Habbeyb, D. S., Barus, S., Pasaribu, B., Sirbu, L. I., & Dharma, A. (2021) · International Journal of Artificial Intelligence Research · doi:10.29099/ijair.v4i2.169 · Cited by 10
“For instance, an HR recruitment model [18]–[20] and employee turnover uplift (ETU) model [21], [22].”
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RetractedData-driven prescriptive analytics applications: A comprehensive survey
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“In addition to identifying the most suitable candidates, Pessach et al. (2020) propose an optimization approach to maximize diversity among selected applicants.”
InfluentialMeasuring employer attractiveness in diverse talent markets
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Improving Machine Leaming Accuracy using Data Augmentation in Recruitment Recommendation Process
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MethodSinger, G., Golan, M., Shiff, R., & Kleper, D. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3214537 · Cited by 7
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“Since sales volume (C) may depend on available customer support and customer satisfaction, it is not a simple or known function of the number of staff (X) and may require causal inference to estimate; see Pessach et al. (2020).”
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Yadav, P. V., Kollimath, U. S., Giramkar, S. A., Pisal, D. T., Badave, S. S., & Swamy, S. M. (2023) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta59349.2023.10293704 · Cited by 11
“Here are some details about AI for the automation of administrative tasks [11] It's important to note that while AI can enhance employee engagement, it should be implemented with transparency, fairness, and privacy considerations in mind.”
BackgroundAnalíticas de recursos humanos para la gestión del cambio y de la felicidad
Abellán-Sevilla, A.-J., Ortiz‐de‐Urbina‐Criado, M., & Valentín, E. M. M. (2024) · Retos · doi:10.17163/ret.n28.2024.03 · Cited by 5
AI and analytics conundrum: unpacking the barriers in modern HR with ISM and MICMAC analysis
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“…have accumulated that evaluated the effectiveness and refined the techniques in the ML literature (e.g., Garg et al. 2022; Jirjees et al. 2025; Pessach et al. 2020; Reddy et al. 2020; Surya et al. 2024), while there is currently only basic awareness of the potential value in the HRM…”
Margherita, A., Gal, H. C., & Topaz, G. (2025) · Applied innovation and technology management · doi:10.1007/978-3-031-80426-7_4 · Cited by 4
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“Collaborative filtering, matrix factorization, and deep learning-based recommendation algorithms can build models based on historical data to predict the jobs candidates may be interested in and recommend the most suitable talent to employers (Köchling & Wehner, 2020; Pessach et al., 2020).”
Talent Recruitment Platform for Large-Scale Group Enterprises Based on Deep Learning
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“Some studies discussed prescriptive analytics framework in the areas of health, electrical, and sales problem [6], while other studies discussed prescriptive analytics implementation in the areas of transportation [7], public sector [8], manufacturing [9], and human resources management [10].”
BackgroundMosli, R. (2024) · IEEE Access · doi:10.1109/access.2024.3457025 · Cited by 6
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“The AI recruitment model reported in this research was trained on a dataset from Pesach and colleagues (2020) [34], which features hundreds of thousands of employees recruited over a decade.”
MethodAssi, P. N., & Effanga, E. O. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07424 · Cited by 4
Predicting Employee Selection using Machine Learning Techniques
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“Developing a strong and rigorous selection process ensures that the organization is hiring the right kind of employees who will not only help an organization to achieve the objectives but at the same time saves money, time and energy on replacing and training new people [4] [5].”
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Masruroh, F., Surarso, B., & Warsito, B. (2022) · AIP conference proceedings · doi:10.1063/5.0109462 · Cited by 4
Artificial Intelligence Applications in Human Resource Management
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“The programme enables educators to produce lesson plans and individualized resources in a matter of minutes as opposed to hours [18].”
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“For employers, the ability to upload and delete vacancies directly within the system is a fundamental feature of the software [9].”
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Kameswari, J., Palivela, H., Settur, S., & Solanki, P. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80382-027-920231013 · Cited by 4
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“Typical planning approaches might make it challenging to properly handle large volumes of sophisticated data [5].”
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“We compare our MCM Algorithm with two heuristic methods for the resource planning application of the PSO: Naïve-Greedy and Intelligent-Greedy, which mimic a typical resource planning process in practice (Pessach et al. 2020).”
Prima, C. D., Martin, Č., Perotti, F. A., & Bresciani, S. (2026) · International Entrepreneurship and Management Journal · doi:10.1007/s11365-026-01178-w · Cited by 3
“Regarding recruitment and selection, HR analytics transforms these processes by leveraging predictive algorithms to identify high-potential candidates and by analyz - ing patterns in past hiring successes and failures (Pessach et al., 2020).”
The resourcing paradox: a systematic review of efficiency and effectiveness in AI-powered recruiting
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“Compared to human assessment, AI-based applications can be trained to avoid susceptibility to cognitive bias based on gender, race, or other factors that might impair judgment [30].”
BackgroundTowards a digital enterprise: the impact of Artificial Intelligence on the hiring process
Amzile, K., Beraich, M., AMOURI, I., & Malainine, C. (2023) · Journal of Intelligence Studies in Business · doi:10.37380/jisib.v12i3.894 · Cited by 3
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Application of Artificial Intelligence in Human Resource Management: Is present or future?
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Shin, G.-H., & Jung, M. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13112127 · Cited by 3
“Pes-sach et al. [21] achieved 86% prediction accuracy by combining Variable-Order Bayesian Networks (VOBN) with HITL methodology, while Al-Quhfa et al. [22] systematically compared nine machine learning algorithms including K-NN, SVM, Random Forest, and Multi-Layer Perceptron, confirming that…”
Comparing the Performance of Machine Learning Algorithms for Predicting Employees’ Turnover
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“Pessach et al. [12] proposed a Variable-Order Bayesian Network (VOBN) model as a hybrid framework using machine learning and global mathematical optimization for pre-hire predicting employees’ recruitment success.”
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AI in higher education: Does not help, might hurt
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“E.g. Workforce planning [11].”
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Classification Tasks with Local and Global Resource Allocation Constraints
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Self-citationA Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics
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“The concept is to train computers to discover trends in data and generate optimal choices with minimal human interaction [7].”
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HUMAN RESOURCE ANALYTICS KEY ROLE IN DIGITALIZING HR PROCESS IN BUSINESS ORGANIZATIONS: A REVIEW
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Analytical Approaches in Human Resources – a Systematic Review
Soares, V. G., Alcázar, J. D. J. P., & Ludeña, M. E. (2022) · Journal of Information Systems and Technology Management · doi:10.4301/s1807-1775202219014 · Cited by 6
Bulsari, S., & Pandya, K. (2023) · Advances in human resources management and organizational development book series · doi:10.4018/978-1-6684-6745-9.ch009 · Cited by 2
Management Implications for HR Analytics in Light of Systems Theory
Peres, A. R., Riccio, É. L., & Laurindo, F. J. B. (2023) · Cadernos de Educação Tecnologia e Sociedade · doi:10.14571/brajets.v16.n2.307-329 · Cited by 3
Comparison of multi-criteria decision aiding methods in the problem of employee recruitment
Ziemba, P. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.262 · Cited by 2
Data-driven internal mobility: Similarity regularization gets the job done
Vos, S. D., Smedt, J. D., Verbruggen, M., & Verbeke, W. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111824 · Cited by 10
Allaymoun, M. H., Alkadash, T. M., Shorman, S. M., & Yousef, M. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-54379-1_41 · Cited by 2
Optimizing employee recruitment: A prescriptive analytics review
Fitha, T. F., Parveen, K. J., Noorfathima, P. P., & Bajeel, P. N. (2024) · AIP conference proceedings · doi:10.1063/5.0234830 · Cited by 2
Predictive Analytics in Recruitment and Selection Practices
Sasirekha, V., Nithyashree, N., & Sarulatha, N. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815274196124010006 · Cited by 2
Andrian, A. M., Khamis, N. K., Subbiah, A., Zuhriyah, H., & Faizah, S. (2024) · IEEE Conference on Systems, Process and Control · doi:10.1109/icspc63060.2024.10862166 · Cited by 2
Raj, G., & Krishnan, T. N. (2025) · Human Resource Management Review · doi:10.1016/j.hrmr.2025.101102 · Cited by 3
Demir, K., & Çalık, E. (2020) · Journal of Business Research - Turk · doi:10.20491/isarder.2020.1070 · Cited by 2
Demircan, M. L., & Aksaç, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-09176-6_3 · Cited by 2
Hunt, W. T., & O'Reilly, J. (2022) · doi:10.20919/alnb9606 · Cited by 2
The ethical shortlisting problem
Erdoğan, G. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2021.105593 · Cited by 2
AutIS: Artificial Intelligent Based Automated Interviewing System
Dewang, R. K., Gupta, A. K., Kumari, A., Raj, R., Gupta, A., Shah, R. N., Jaiswal, T., & Mewada, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96305-7_29 · Cited by 1
Chen, W., Ngo, A.-X., & Chang, H.-P. (2024) · Mathematics · doi:10.3390/math12132094 · Cited by 1
Stock Data Prediction and Visualization using Advanced Learning and Dash Framework
Praveen, P., Karre, R., & Vinay, B. (2024) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc60852.2024.10689955 · Cited by 1
“[2] proposed a stock forecasting model.”
AI in Project Resource Management
Ranković, N., Ranković, D., Ivanović, M. D., & Lazić, L. (2024) · Artificial intelligence-enhanced software and systems engineering · doi:10.1007/978-3-031-76572-8_6 · Cited by 2
Benabou, A., & Touhami, F. (2025) · Management and Accounting Review · doi:10.24191/mar.v24i01-15 · Cited by 5
“AI uses big data analytics to predict compensation trends, monitor labor market shifts, and adjust worker compensation based on performance, thereby motivating employees and aiding HR managers in designing equitable compensation plans (Pessach et al., 2020).”
Impact of Organizational Culture and Workforce Diversity on Management Practices
El-Khatib, S. E.-S., Aboeldahab, M., & Elhegazy, H. M. (2025) · Employee Responsibilities and Rights Journal · doi:10.1007/s10672-025-09540-w · Cited by 1
A Comprehensive Review of Soft Skills Training: Enhancing Employability and Impact on Job Market
Banu, K. A. S., & Lakshmi, T. R. K. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-84513-0_8 · Cited by 1
Shi, W., Zhao, P.-C., & Luo, Y. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625504791 · Cited by 1
Fairness in Machine Learning: A Review for Statisticians
He, X., & Li, Y. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2579579 · Cited by 2
“…in various aspects of our daily life, such as education (Kuˇcak, Juriˇci´c, and Džambi´c 2018; Luan and Tsai 2021), labor market (Faliagka et al. 2012; Pessach et al. 2020), medicine (Chen et al. 2021; Shehab et al. 2022), entertainment (Liao et al. 2024), and legal systems (Northpointe Inc.…”
The Impact of Predictive Analytics on Recruitment: Going Beyond Traditional Methods
Nayak, A., & Satpathy, I. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898811266125050010 · Cited by 1
Lim, J., & Jang, J. (2025) · Cornell Hospitality Quarterly · doi:10.1177/19389655251383892 · Cited by 1
“…achieve the research objectives, this study applies a people analytics approach, a novel approach in the HR context, which holds the benefits of effectively collecting data and analyzing actual employees’ behaviors beyond attitudes and intentions in surveys (Margherita, 2022; Pessach et al., 2020).”
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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