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.

中文翻译：

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使用协作主题回归模型加强雇主品牌评估

雇主品牌评估（EBE）是了解雇主的独特特征以识别竞争优势。传统方法严重依赖雇主的财务信息，包括提交给证券交易委员会 (SEC) 的财务报告和文件，而私营公司可能不容易获得这些信息。幸运的是，在线招聘服务从员工的在线评分和评论中提供了各种雇主信息，这使得 EBE 能够从员工的角度进行。为此，在本文中，我们提出了一种名为 Company Profiling-based Collaborative Topic Regression (CPCTR) 的方法，用于协同建模文本（即评论）和数字信息（即薪水和评级），以学习雇主的潜在结构模式品牌。有了确定的模式，我们可以有效地进行定性意见分析和定量薪酬对标。此外，提出了一种基于高斯过程的扩展 GPCTR 来捕获异构信息之间的复杂相关性。在三个真实世界的数据集上进行了广泛的实验，以验证我们的方法在实际应用中的有效性和普遍性。结果清楚地表明，我们的方法优于最先进的基线，并能够全面了解 EBE。在三个真实世界的数据集上进行了广泛的实验，以验证我们的方法在实际应用中的有效性和普遍性。结果清楚地表明，我们的方法优于最先进的基线，并能够全面了解 EBE。在三个真实世界的数据集上进行了广泛的实验，以验证我们的方法在实际应用中的有效性和普遍性。结果清楚地表明，我们的方法优于最先进的基线，并能够全面了解 EBE。