# Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

arXiv: https://arxiv.org/abs/1810.04040 · HTML: https://mlsanity.com/paper/1810.04040/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting](https://mlsanity.com/paper/2405.18113/) (2024)
- [DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation](https://mlsanity.com/paper/2410.07671/) (2024)
- [SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://mlsanity.com/paper/2302.09938/) (2023)
- [ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning](https://mlsanity.com/paper/2401.16349/) (2024)
- [MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model](https://mlsanity.com/paper/2508.13676/) (2025)

## Abstract (arXiv)

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.