  The online recruitment matching system has been the core technology and
service platform in CareerBuilder. One of the major challenges in an online
recruitment scenario is to provide good matches between job posts and
candidates using a recommender system on the scale. In this paper, we discussed
the techniques for applying an embedding-based recommender system for the large
scale of job to candidates matching. To learn the comprehensive and effective
embedding for job posts and candidates, we have constructed a fused-embedding
via different levels of representation learning from raw text, semantic
entities and location information. The clusters of fused-embedding of job and
candidates are then used to build and train the Faiss index that supports
runtime approximate nearest neighbor search for candidate retrieval. After the
first stage of candidate retrieval, a second stage reranking model that
utilizes other contextual information was used to generate the final matching
result. Both offline and online evaluation results indicate a significant
improvement of our proposed two-staged embedding-based system in terms of
click-through rate (CTR), quality and normalized discounted accumulated gain
(nDCG), compared to those obtained from our baseline system. We further
described the deployment of the system that supports the million-scale job and
candidate matching process at CareerBuilder. The overall improvement of our job
to candidate matching system has demonstrated its feasibility and scalability
at a major online recruitment site.
