  Job recommendation is a crucial part of the online job recruitment business.
To match the right person with the right job, a good representation of job
postings is required. Such representations should ideally recommend jobs with
fitting titles, aligned skill set, and reasonable commute. To address these
aspects, we utilize three information graphs ( job-job, skill-skill, job-skill)
from historical job data to learn a joint representation for both job titles
and skills in a shared latent space. This allows us to gain a representation of
job postings/ resume using both elements, which subsequently can be combined
with location. In this paper, we first present how the presentation of each
component is obtained, and then we discuss how these different representations
are combined together into one single space to acquire the final
representation. The results of comparing the proposed methodology against
different base-line methods show significant improvement in terms of relevancy.
