  Job recommendation has traditionally been treated as a filter-based match or
as a recommendation based on the features of jobs and candidates as discrete
entities. In this paper, we introduce a methodology where we leverage the
progression of job selection by candidates using machine learning.
Additionally, our recommendation is composed of several other
sub-recommendations that contribute to at least one of a) making
recommendations serendipitous for the end user b) overcoming cold-start for
both candidates and jobs. One of the unique selling propositions of our
methodology is the way we have used skills as embedded features and derived
latent competencies from them, thereby attempting to expand the skills of
candidates and jobs to achieve more coverage in the skill domain. We have
deployed our model in a real-world job recommender system and have achieved the
best click-through rate through a blended approach of machine-learned
recommendations and other sub-recommendations. For recommending jobs through
machine learning that forms a significant part of our recommendation, we
achieve the best results through Bi-LSTM with attention.
