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

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
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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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Index Terms

Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
1. Information systems
  1. Data management systems
    1. Information integration
      1. Wrappers (data mining)
  2. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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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

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
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
https://dl.acm.org/doi/10.1145/3659942
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
https://dl.acm.org/doi/10.1145/3626772.3657776
Chen HDu LLu YFu QChen XHan SKang YLu GLi ZAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Professional Network Matters: Connections Empower Person-Job FitProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635852(96-105)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635852
Zheng ZHu XQiu ZCheng YGao SSong YZhu HXiong H(2024)Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online RecruitmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339770536:11(5681-5694)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3397705
Li JYao JTeng GYan M(2024)PTCR-PJF: A Person-Job Fit Model for Structured Resumes2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651251(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651251
Gaddam JBarca JNguven TAngelova M(2024)Job-Profile Matching with CTN and MADRL with GEABB: A Recommender System2024 16th International Conference on Computer and Automation Engineering (ICCAE)10.1109/ICCAE59995.2024.10569707(203-210)Online publication date: 14-Mar-2024
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Ya JDeng ZGao H(2024)Enhancing Reciprocal Recommendation with Bidirectional Global-Local Insights2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)10.1109/CCAI61966.2024.10603077(314-319)Online publication date: 24-May-2024
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De Vos SDe Smedt JVerbruggen MVerbeke W(2024)Data-driven internal mobility: Similarity regularization gets the job doneKnowledge-Based Systems10.1016/j.knosys.2024.111824295(111824)Online publication date: Jul-2024
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Rahhal IKassou IGhogho M(2024)Data science for job market analysis: A survey on applications and techniquesExpert Systems with Applications10.1016/j.eswa.2024.124101251(124101)Online publication date: Oct-2024
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