The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 1×
“…with position dropout rates in specific business units for male candidates. a Note that both the RF and GBM models and their implementations are generally robust to noisy and high dimensionality datasets, since they base their decisions on multiple permutations of the dataset (see [56,66–69]).”
BackgroundA working guide to boosted regression trees
Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484 · cited 1×
“The best prediction was obtained using the GBM algorithm [56], with AUC = 0.”
MethodSupervised Machine Learning: A Review of Classification Techniques
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Wright, M. N., & Ziegler, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v077.i01 · Cited by 4,185 · cited 1×
“…with position dropout rates in specific business units for male candidates. a Note that both the RF and GBM models and their implementations are generally robust to noisy and high dimensionality datasets, since they base their decisions on multiple permutations of the dataset (see [56,66–69]).”
BackgroundData Mining for Imbalanced Datasets: An Overview
Chawla, N. V. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_45 · Cited by 1,197 · cited 2×
“According to the literature, e.g., Chawla [51], when the dataset is imbalanced (e.g., when the target variable includes large differences between the frequencies of different class values), an appropriate performance measure is the ROC curve and the AUC measure.”
BackgroundCoomber, B., & Barriball, K. L. (2006) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2006.02.004 · Cited by 939
On Simpson's Paradox and the Sure-Thing Principle
Blyth, C. R. (1972) · Journal of the American Statistical Association · doi:10.1080/01621459.1972.10482387 · Cited by 640 · cited 2×
“This notion is also closely related to Simpson's Paradox [55], which shows that an observed trend in subgroups may behave quite differently (even reversely) when these subgroups are aggregated and analyzed together.”
ResultLinear and Nonlinear Optimization
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Comprehensible classification models
Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 575 · cited 3×
“The results of our evaluation reveal that the proposed prediction approach can perform well in terms of both accuracy and interpretability, despite the inherent trade-off that often exists between the two [9,12].”
MethodResultAssignment problems: A golden anniversary survey
Pentico, D. W. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.09.014 · Cited by 512 · cited 1×
“The first formulation (Formulation 1) is used for benchmark purposes and is a rather simple adjustment to the assignment problem [50], in which the objective function (1.”
Background
Flagged first, then by citations
Research works printed but not linked: 4
- Bernthal, 2001, Retaining talent: a benchmarking study, HR Benchmark Gr, 2, 1
- Ribes, 2017, Employee turnover prediction and retention policies design: a case study, ArXiv Prepr, ArXiv1707, 01377
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- Ben-Gal, 2003, Context-based statistical process control: a monitoring procedure for state-dependent processes, Technometrics, 45, 293, 10.1198/004017003000000122 · DOI
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Sachdeva, G., & Rana, A. (2026) · International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds69611.2026.11723499
Obi, A. V., & Yoruba, E. F. (2026) · FUDMA Journal of Business Management · doi:10.70882/fjbm.2026.v4i2.135
Yi, W., Cai, F., 陈丽萍, & Lin, B. (2026) · International Conference on Artificial Intelligence and Digital Finance (AIDF) · doi:10.1145/3844275.3844348
“While traditional machine learning systems have attempted to provide personalized pathways, they often struggle with severe cold-start problems and the high maintenance costs associated with unstructured HR data [4].”
Abylaikhanova, T., Domalatov, Y., Rakhmetulina, Z. B., Kenzhin, Z. B., Rakhmetulina, A., Bukhatova, A., & Narimbetova, A. (2026) · Problems and Perspectives in Management · doi:10.21511/ppm.24(4).2026.16
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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