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

TL;DR

This paper introduces a novel heterogeneous graph model that enriches job transition data with tags to improve job title representation learning, addressing data sparsity issues in traditional methods.

Contribution

It proposes the Job-Transition-Tag Graph, a new approach that incorporates tags into job transition graphs for better representation learning.

Findings

Enhanced job title representations demonstrated on two datasets.

The enriched graph improves the quality of learned embeddings.

The approach addresses sparsity issues in traditional job transition graphs.

Abstract

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 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 \textit{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 functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the \textit{Job-Transition-Tag Graph}. Experiments on two datasets show the interest of our approach.