Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning. (arXiv:2206.02782v1 [cs.LG])

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.



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