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Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming by Dana Pessach; Gonen Singer; Dan Avrahami; Hila Chalutz Ben-Gal; Erez Shmueli; Irad Ben-Gal is a Computer Science article available to read on EtoBox.

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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

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Author

Dana Pessach; Gonen Singer; Dan Avrahami; Hila Chalutz Ben-Gal; Erez Shmueli; Irad Ben-Gal

Published

2020

Language

EN

Field

Computer Science (Social Sciences)