Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model

Qingxin Meng (), Keli Xiao (), Dazhong Shen (), Hengshu Zhu () and Hui Xiong ()
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Qingxin Meng: University of Nottingham Ningbo China, Ningbo, Zhejiang 315104, China
Keli Xiao: Stony Brook University, Stony Brook, New York 11794
Dazhong Shen: University of Science and Technology of China, Hefei, Anhui 230052, China
Hengshu Zhu: Baidu Talent Intelligence Center, Baidu Inc., Haidian, Beijing 100085, China
Hui Xiong: AI Thrust, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

INFORMS Journal on Computing, 2022, vol. 34, issue 5, 2443-2463

Abstract: As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management. Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.

Keywords: job salary benchmarking; nonparametric dirichlet process; latent factor model; talent management (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:5:p:2443-2463

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