Similarity computation exploiting the semantic and syntactic inherent structure among job titles

Abstract

Solutions providing hiring analytics involve mapping company provided job descriptions to a standard job framework, thereby requiring computation of a similarity score between two jobs. Most systems doing so apply document similarity computation methods to all pairs of provided job descriptions. This approach can be computationally expensive and adversely impacted by the quality of the job descriptions which often include information not relevant to the job or candidate qualifications. We propose a method to narrow down pairs of job descriptions to be compared by comparing job titles first. The observation that each job title can be decomposed into three components, domain, function and attribute, forms the basis of our method. Our proposal focuses on training the machine learning models to identify these three components of any given job title. Next we do a semantic match between the three identified components, and use those match scores to create a composite similarity score between any two pair of job titles. The elegance of this solution lies in the fact that job titles are the most concise definition of the job and the resulting matches can easily be verified by human experts. Our results show that the approach provides extremely reliable results.