Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning

Anonymous

Abstract: Works on learning job title representation are mainly based on Job-Transition Graph, built from the working history of talents. However, since the records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct Job-Transition-Tag Graph, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functions). Along this line, we reformulate job title representation learning as the task of learning node embedding on the Job-Transition-Tag Graph. Experiments on a public CareerBuilder12 dataset and a private Randstad dataset show interest of our approach.