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.

本文研究基于嵌入的推荐系统应用于大规模的招聘场景，提出融合不同级别的文本、语义实体和位置信息的嵌入式聚类方法构建 Faiss 索引来支持候选人检索，最终运用二阶段模型完成了在线招聘匹配系统，并取得了显著的成功。