Job2Vec: Job Title Benchmarking with Collective Multi-View
  Representation Learning
  Job Title Benchmarking (JTB) aims at matching job titles with similar
expertise levels across various companies. JTB could provide precise guidance
and considerable convenience for both talent recruitment and job seekers for
position and salary calibration/prediction. Traditional JTB approaches mainly
rely on manual market surveys, which is expensive and labor-intensive.
Recently, the rapid development of Online Professional Graph has accumulated a
large number of talent career records, which provides a promising trend for
data-driven solutions. However, it is still a challenging task since (1) the
job title and job transition (job-hopping) data is messy which contains a lot
of subjective and non-standard naming conventions for the same position (e.g.,
Programmer, Software Development Engineer, SDE, Implementation Engineer), (2)
there is a large amount of missing title/transition information, and (3) one
talent only seeks limited numbers of jobs which brings the incompleteness and
randomness modeling job transition patterns. To overcome these challenges, we
aggregate all the records to construct a large-scale Job Title Benchmarking
Graph (Job-Graph), where nodes denote job titles affiliated with specific
companies and links denote the correlations between jobs. We reformulate the
JTB as the task of link prediction over the Job-Graph that matched job titles
should have links. Along this line, we propose a collective multi-view
representation learning method (Job2Vec) by examining the Job-Graph jointly in
(1) graph topology view, (2)semantic view, (3) job transition balance view, and
(4) job transition duration view. We fuse the multi-view representations in the
encode-decode paradigm to obtain a unified optimal representation for the task
of link prediction. Finally, we conduct extensive experiments to validate the
effectiveness of our proposed method.
