# Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

arXiv: https://arxiv.org/abs/1812.08947 · HTML: https://mlsanity.com/paper/1812.08947/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 30 of 39)

- [Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models](https://mlsanity.com/paper/2203.01584/) (2022)
- [Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations](https://mlsanity.com/paper/2307.05722/) (2023)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
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- [Understanding Intrinsic Socioeconomic Biases in Large Language Models](https://mlsanity.com/paper/2405.18662/) (2024)
- [MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting](https://mlsanity.com/paper/2405.18113/) (2024)
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- [EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator](https://mlsanity.com/paper/2301.00972/) (2023)
- [LinkSAGE: Optimizing Job Matching Using Graph Neural Networks](https://mlsanity.com/paper/2402.13430/) (2024)
- [ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining](https://mlsanity.com/paper/2502.12361/) (2025)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)
- [Fake Resume Attacks: Data Poisoning on Online Job Platforms](https://mlsanity.com/paper/2402.14124/) (2024)
- [JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer](https://mlsanity.com/paper/2404.04313/) (2024)
- [Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach](https://mlsanity.com/paper/2404.13067/) (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)
- [DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation](https://mlsanity.com/paper/2410.07671/) (2024)
- [ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking](https://mlsanity.com/paper/2605.09760/) (2026)
- [Explainability-aided Domain Generalization for Image Classification](https://mlsanity.com/paper/2104.01742/) (2021)
- [Improving Next-Application Prediction with Deep Personalized-Attention Neural Network](https://mlsanity.com/paper/2111.11296/) (2021)
- [SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://mlsanity.com/paper/2302.09938/) (2023)
- [Detecting Fake Job Postings Using Bidirectional LSTM](https://mlsanity.com/paper/2304.02019/) (2023)
- [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)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [Fairness in Federated Learning: Fairness for Whom?](https://mlsanity.com/paper/2505.21584/) (2025)
- [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)

## Abstract (arXiv)

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end to end Ability aware Person Job Fit Neural Network model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word level semantic representation for both job requirements and job seekers' experiences based on Recurrent Neural Network. Along this line, four hierarchical ability aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large scale real world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines.