TY - JOUR
T1 - Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models
AU - Lin, Hao
AU - Zhu, Hengshu
AU - Wu, Junjie
AU - Zuo, Yuan
AU - Zhu, Chen
AU - Xiong, Hui
N1 - Funding Information:
A preliminary version of this article has been published in the 31st AAAI Conference on Artificial Intelligence (AAAI-17) [Lin et al. 2017]. Dr. Junjie Wu’s work was partially supported by the National Key R&D Program of China (2019YFB2101804), the National Special Program on Innovation Methodologies (SQ2019IM4910001), and the National Natural Science Foundation of China (71725002, 71531001, U1636210). Dr. Yuan Zuo was supported by the National Natural Science Foundation of China under Grant 71901012 and the China Postdoctoral Science Foundation under Grant 2018M640045. Authors’ addresses: H. Lin and Y. Zuo, School of Economics and Management, Beihang University, No. 37 Xue Yuan Road, Haidian District, Beijing, 100191, China; emails: {linhao2014, zuoyuan}@buaa.edu.cn; H. Zhu (corresponding author) and C. Zhu, Talent Intelligence Center, Baidu Inc., 10 Shangdi 10th Street, Haidian District, Beijing, 100085, China; emails: zhuhengshu@gmail.com, zhuchen02@baidu.com; J. Wu (corresponding author), School of Economics and Management, Beihang University, No. 37 Xue Yuan Road, Haidian District, Beijing, 100191, China, and Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xue Yuan Road, Haidian District, Beijing, 100191, China; email: wujj@buaa.edu.cn; H. Xiong, Management Science and Information Systems Department, Rutgers University, Newark, NJ, 07102, USA; email: hxiong@rutgers.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 1046-8188/2020/05-ART32 $15.00 https://doi.org/10.1145/3392734
Publisher Copyright:
© 2020 ACM.
PY - 2020/10
Y1 - 2020/10
N2 - Employer Brand Evaluation (EBE) is to understand an employer's unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers' financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers' information from their employees' online ratings and comments, which enables EBE from an employee's perspective. To this end, in this article, we propose a method named Company Profiling-based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes-based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-The-Art baselines and enable a comprehensive understanding of EBE.
AB - Employer Brand Evaluation (EBE) is to understand an employer's unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers' financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers' information from their employees' online ratings and comments, which enables EBE from an employee's perspective. To this end, in this article, we propose a method named Company Profiling-based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes-based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-The-Art baselines and enable a comprehensive understanding of EBE.
KW - Employer brand evaluation
KW - Gaussian processes
KW - collaborative topic regression
KW - salary benchmarking
UR - http://www.scopus.com/inward/record.url?scp=85093971149&partnerID=8YFLogxK
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U2 - 10.1145/3392734
DO - 10.1145/3392734
M3 - Article
AN - SCOPUS:85093971149
VL - 38
JO - ACM Transactions on Office Information Systems
JF - ACM Transactions on Office Information Systems
SN - 1046-8188
IS - 4
M1 - 3392734
ER -