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

TL;DR

This paper introduces PJFNN, a neural network model that quantitatively measures person-job fit by learning joint representations from historical data, enabling both fit prediction and requirement identification.

Contribution

The paper presents a novel CNN-based bipartite neural network that estimates person-job fit and identifies specific matched requirements, advancing quantitative assessment methods.

Findings

PJFNN outperforms existing models in fit prediction accuracy.

The model effectively identifies which job requirements are satisfied by candidates.

Data visualization provides new insights into talent and job benchmarks.

Abstract

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.