# Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

arXiv: https://arxiv.org/abs/2009.13299 · HTML: https://mlsanity.com/paper/2009.13299/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations](https://mlsanity.com/paper/2307.05722/) (2023)
- [Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT](https://mlsanity.com/paper/2310.05135/) (2023)
- [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)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions](https://mlsanity.com/paper/2407.05458/) (2024)
- [ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining](https://mlsanity.com/paper/2502.12361/) (2025)
- [Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks](https://mlsanity.com/paper/2311.06383/) (2023)
- [JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer](https://mlsanity.com/paper/2404.04313/) (2024)
- [Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking](https://mlsanity.com/paper/2406.11920/) (2024)
- [Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval](https://mlsanity.com/paper/2409.12097/) (2024)
- [ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking](https://mlsanity.com/paper/2605.09760/) (2026)
- [TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit](https://mlsanity.com/paper/2401.07525/) (2024)
- [ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning](https://mlsanity.com/paper/2401.16349/) (2024)
- [Learning to Retrieve for Job Matching](https://mlsanity.com/paper/2402.13435/) (2024)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fit](https://mlsanity.com/paper/2601.10321/) (2026)
- [Toward a traceable, explainable, and fairJD/Resume recommendation system](https://mlsanity.com/paper/2202.08960/) (2022)
- [Professional Network Matters: Connections Empower Person-Job Fit](https://mlsanity.com/paper/2401.00010/) (2023)
- [MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model](https://mlsanity.com/paper/2508.13676/) (2025)
- [Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data](https://mlsanity.com/paper/2604.17738/) (2026)
- [How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications](https://mlsanity.com/paper/2604.22679/) (2026)
- [Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval](https://mlsanity.com/paper/2609.23307/) (2026)
- [Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals](https://mlsanity.com/paper/2610.03112/) (2026)
- [EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI](https://mlsanity.com/paper/2610.16689/) (2026)

## Abstract (arXiv)

With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. To alleviate these problems, in this paper, we propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our network consists of two major components, namely text-based matching model and relation-based matching model. The two parts capture semantic compatibility in two different views, and complement each other. In order to address the challenges from sparse and noisy data, we design two specific strategies to combine the two components. First, two components share the learned parameters or representations, so that the original representations of each component can be enhanced. More importantly, we adopt a co-teaching mechanism to reduce the influence of noise in training data. The core idea is to let the two components help each other by selecting more reliable training instances. The two strategies focus on representation enhancement and data enhancement, respectively. Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data. Experiment results have demonstrated that our model is able to outperform state-of-the-art methods for job-resume matching.