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The online recruitment matching system has been the core technology andservice platform in CareerBuilder. One of the major challenges in an onlinerecruitment scenario is to provide good matches between job posts andcandidates using a recommender system on the scale. In this paper, we discussedthe techniques for applying an embedding-based recommender system for the largescale of job to candidates matching. To learn the comprehensive and effectiveembedding for job posts and candidates, we have constructed a fused-embeddingvia different levels of representation learning from raw text, semanticentities and location information. The clusters of fused-embedding of job andcandidates are then used to build and train the Faiss index that supportsruntime approximate nearest neighbor search for candidate retrieval. After thefirst stage of candidate retrieval, a second stage reranking model thatutilizes other contextual information was used to generate the final matchingresult. Both offline and online evaluation results indicate a significantimprovement of our proposed two-staged embedding-based system in terms ofclick-through rate (CTR), quality and normalized discounted accumulated gain(nDCG), compared to those obtained from our baseline system. We furtherdescribed the deployment of the system that supports the million-scale job andcandidate matching process at CareerBuilder. The overall improvement of our jobto candidate matching system has demonstrated its feasibility and scalabilityat a major online recruitment site.

Embedding-based Recommender System for Job to Candidate Matching on Scale