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Works on learning job title representation are mainly based on\textit{Job-Transition Graph}, built from the working history of talents.However, since these records are usually messy, this graph is very sparse,which affects the quality of the learned representation and hinders furtheranalysis. To address this specific issue, we propose to enrich the graph withadditional nodes that improve the quality of job title representation.Specifically, we construct \textit{Job-Transition-Tag Graph}, a heterogeneousgraph containing two types of nodes, i.e., job titles and tags (i.e., wordsrelated to job responsibilities or functionalities). Along this line, wereformulate job title representation learning as the task of learning nodeembedding on the \textit{Job-Transition-Tag Graph}. Experiments on two datasetsshow the interest of our approach.

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