Employer name normalization, or linking employer names in job postings or resumes to entities in an employer knowledge base (KB), is important for many downstream applications in the online recruitment domain. Key challenges for employer name normalization include handling employer names from both job postings and resumes, leveraging the corresponding location and URL context, and handling name variations and duplicates in the KB. In this paper, we describe the CompanyDepot system developed at CareerBuilder, which uses machine learning techniques to address these challenges. We discuss the main challenges and share our lessons learned in deployment, maintenance, and utilization of the system over the past two years. We also share several examples of how the system has been used in applications at CareerBuilder to deliver value to end customers.

In the recruitment domain, knowing the employer industry of jobs is important to get an insight about the demand in each industry. The existing system at CareerBuilder uses an employer name normalization system and an employer knowledge base (KB) to infer the employer industry of a job. However, errors may occur during the computation of the job employer and in the construction of the employer KB with the industry attributes. Since the KB is huge, it is not possible to manually detect the errors. Therefore, in this paper we use machine learning techniques to automatically detect the errors. With the observation that the main jobs posted by an employer often relate to the employer industry, e.g., truck driver jobs often correspond to employers in the transportation industry, we develop a system that classifies the industry of an employer using job posting data. We aggregate job postings from an employer and derive features from employer names, employer descriptions, job titles, and job descriptions to predict the industry of the employer. Two models are used for classification: (1) support vector machine and (2) random forest. Our experiments show that random forest is more effective than SVM in identifying the errors in the existing industry classification system, which achieves precision 0.69, recall 0.78, and f-score 0.73. It especially better handles mixed feature vectors when normalization errors occur. We also observe that generally our models perform better in detecting errors for industries that have higher error rates.