Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models

Hao Lin, Hengshu Zhu, Junjie Wu, Yuan Zuo, Chen Zhu, Hui Xiong

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article number3392734
JournalACM Transactions on Information Systems
Issue number4
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications


  • Employer brand evaluation
  • Gaussian processes
  • collaborative topic regression
  • salary benchmarking


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