  Online job boards are one of the central components of modern recruitment
industry. With millions of candidates browsing through job postings everyday,
the need for accurate, effective, meaningful, and transparent job
recommendations is apparent more than ever. While recommendation systems are
successfully advancing in variety of online domains by creating social and
commercial value, the job recommendation domain is less explored. Existing
systems are mostly focused on content analysis of resumes and job descriptions,
relying heavily on the accuracy and coverage of the semantic analysis and
modeling of the content in which case, they end up usually suffering from
rigidity and the lack of implicit semantic relations that are uncovered from
users' behavior and could be captured by Collaborative Filtering (CF) methods.
Few works which utilize CF do not address the scalability challenges of
real-world systems and the problem of cold-start. In this paper, we propose a
scalable item-based recommendation system for online job recommendations. Our
approach overcomes the major challenges of sparsity and scalability by
leveraging a directed graph of jobs connected by multi-edges representing
various behavioral and contextual similarity signals. The short lived nature of
the items (jobs) in the system and the rapid rate in which new users and jobs
enter the system make the cold-start a serious problem hindering CF methods. We
address this problem by harnessing the power of deep learning in addition to
user behavior to serve hybrid recommendations. Our technique has been leveraged
by CareerBuilder.com which is one of the largest job boards in the world to
generate high-quality recommendations for millions of users.
