research-article

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

Authors:

Pengliang Ding,

Pan LiAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 9, Issue 3

Article No.: 12, Pages 1 - 17

https://doi.org/10.1145/3234465

Published: 26 September 2018 Publication History

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 quantitative ways of measuring talent competencies as well as the job’s talent requirements. To this end, in this article, we propose a novel end-to-end data-driven model based on a Convolutional Neural Network (CNN), namely, the 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 that 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.

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Cited By

Qin CFang CYao KChen XZhuang FZhu H(2025)COTR: Efficient Job Task Recognition for Occupational Information Systems with Class-Incremental LearningACM Transactions on Management Information Systems10.1145/3712306Online publication date: 14-Jan-2025
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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 9, Issue 3

Research Commentary and Regular Papers

September 2018

106 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3281626

Editor:
Daniel Zeng
University of Arizona, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2018

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 January 2018

Published in TMIS Volume 9, Issue 3

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Cited By

Qin CFang CYao KChen XZhuang FZhu H(2025)COTR: Efficient Job Task Recognition for Occupational Information Systems with Class-Incremental LearningACM Transactions on Management Information Systems10.1145/3712306Online publication date: 14-Jan-2025
https://doi.org/10.1145/3712306
Revillod G(2025)Exploring the Acceptability of Artificial Intelligence in Human Resources Management: Insights From Swiss OrganizationsSystems Research and Behavioral Science10.1002/sres.3140Online publication date: 18-Feb-2025
https://doi.org/10.1002/sres.3140
Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030485
Chen CKe B(2024)Machine Learning as a Management Control Mechanism: The Case of Employee SelectionSSRN Electronic Journal10.2139/ssrn.4789953Online publication date: 2024
https://doi.org/10.2139/ssrn.4789953
Lai KYang ZLai PWang CGuizani MChen MWooldridge MDy JNatarajan S(2024)Knowledge-aware explainable reciprocal recommendationProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28708(8636-8644)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28708
Gong ZSong YZhang TWen JZhao DYan RWooldridge MDy JNatarajan S(2024)Your career path matters in person-job fitProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28685(8427-8435)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28685
Du YLuo DYan RWang XLiu HZhu HSong YZhang JWooldridge MDy JNatarajan S(2024)Enhancing job recommendation through LLM-based generative adversarial networksProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28678(8363-8371)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28678
Guan ZYang JYang YZhu HLi WXiong H(2024)JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced TransformerACM Transactions on Knowledge Discovery from Data10.1145/370173519:1(1-20)Online publication date: 26-Oct-2024
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Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
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Zheng ZHu XGao SZhu HXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MIRROR: A Multi-View Reciprocal Recommender System for Online RecruitmentProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657776(543-552)Online publication date: 10-Jul-2024
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