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

arXiv: https://arxiv.org/abs/2107.00221 · HTML: https://mlsanity.com/paper/2107.00221/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2021.

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers](https://mlsanity.com/paper/2109.06501/) (2021)
- [When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs](https://mlsanity.com/paper/2309.14488/) (2023)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (2025)
- [Learning Job Title Representation from Job Description Aggregation Network](https://mlsanity.com/paper/2406.08055/) (2024)
- [Measuring Gender Bias in Job Title Matching for Grammatical Gender Languages](https://mlsanity.com/paper/2509.13803/) (2025)
- [An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit](https://mlsanity.com/paper/2601.10321/) (2026)
- [Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs](https://mlsanity.com/paper/2411.15182/) (2024)
- [Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails](https://mlsanity.com/paper/2508.20328/) (2025)
- [A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments](https://mlsanity.com/paper/2603.21699/) (2026)
- [Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization](https://mlsanity.com/paper/2604.02539/) (2026)
- [TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2607.20009/) (2026)

## Abstract (arXiv)

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.