  Talent search and recommendation systems at LinkedIn strive to match the
potential candidates to the hiring needs of a recruiter or a hiring manager
expressed in terms of a search query or a job posting. Recent work in this
domain has mainly focused on linear models, which do not take complex
relationships between features into account, as well as ensemble tree models,
which introduce non-linearity but are still insufficient for exploring all the
potential feature interactions, and strictly separate feature generation from
modeling. In this paper, we present the results of our application of deep and
representation learning models on LinkedIn Recruiter. Our key contributions
include: (i) Learning semantic representations of sparse entities within the
talent search domain, such as recruiter ids, candidate ids, and skill entity
ids, for which we utilize neural network models that take advantage of LinkedIn
Economic Graph, and (ii) Deep models for learning recruiter engagement and
candidate response in talent search applications. We also explore learning to
rank approaches applied to deep models, and show the benefits for the talent
search use case. Finally, we present offline and online evaluation results for
LinkedIn talent search and recommendation systems, and discuss potential
challenges along the path to a fully deep model architecture. The challenges
and approaches discussed generalize to any multi-faceted search engine.
