How to Match Jobs and Candidates - A Recruitment Support System Based on Feature Engineering and Advanced Analytics

Janusz, Andrzej; Stawicki, Sebastian; Drewniak, Michał; Ciebiera, Krzysztof; Ślęzak, Dominik; Stencel, Krzysztof

doi:10.1007/978-3-319-91476-3_42

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…In recent years, several preliminary studies have focused on predicting recruiters' decisions [5,6,[13][14][15]. However, imitating the recruiter's decision may not necessarily be the best approach, since they are often affected by highly subjective and potentially inaccurate judgments that preserve, rather than improve, hiring biases.…”

Section: Functional Dimensionmentioning

confidence: 99%

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Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

147

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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 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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Section: Functional Dimensionmentioning

confidence: 99%

“…Previous efforts have been invested in trying to predict recruiters' decisions (e.g., [5,6]). Such prediction models, if accurate enough, may eventually replace the human recruiter and save a considerable amount of resources.…”

Section: Introductionmentioning

confidence: 99%

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

147

View full text Add to dashboard Cite

show abstract

“…During such expert validation, the quality of recommendations is inquired by a group of 'experts', which may be the researchers themselves, HR/recruitment experts, or sometimes students (e.g., [85]). Although the choice for expert validation is rarely discussed, we do find that for CBR and KB job recommender systems, approximately half of the contributions use expert validation [63,64,27,65,9,127,128,57,115,88,109,45].…”

Section: Validationmentioning

confidence: 96%

“…In CBRs, one creates vector representations of the vacancy and user profile in an unsupervised way, i.e., the dimensions of these representations may not have an intuitive interpretation. Many authors use Bag of Words (BoW) with TF-IDF weighting [90,63,28,37,27], though also Latent Dirichlet Allocation is used [9], and the more recent word2vec [45,115,57]. Interestingly, CBR contributions have been relatively stable in the past 10 years, but also, they were not part of the top contributions during the 2016 and 2017 RecSys competitions (see Table 1).…”

Section: Content-based Jrsmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

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e-Recruitment recommender systems: a systematic review

Freire

Castro

2020

Knowl Inf Syst

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How to Match Jobs and Candidates - A Recruitment Support System Based on Feature Engineering and Advanced Analytics

Cited by 8 publications

References 13 publications

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Job Recommender Systems: A Review

e-Recruitment recommender systems: a systematic review

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