research-article

Modeling Two-Way Selection Preference for Person-Job Fit

Authors:

Wayne Xin ZhaoAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 102 - 112

https://doi.org/10.1145/3523227.3546752

Published: 13 September 2022 Publication History

Abstract

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN .

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Cited By

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
Yang CDai SHou YZhao WXu JSong YZhu HBaeza-Yates RBonchi F(2024)Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and MethodProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671734(3714-3723)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671734
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

Modeling Two-Way Selection Preference for Person-Job Fit
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

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Cited By

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
Yang CDai SHou YZhao WXu JSong YZhu HBaeza-Yates RBonchi F(2024)Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and MethodProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671734(3714-3723)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671734
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
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: Nov-2024
https://doi.org/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
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
https://doi.org/10.1109/CCAI61966.2024.10603077
Cai QWang YZhang MLi HHan L(2024)A Text-Based Person-Job Matching Model Integrating Structured FeaturesProceedings of 2024 Chinese Intelligent Systems Conference10.1007/978-981-97-8650-3_39(384-392)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-981-97-8650-3_39
Wu XLiu KWang JYao JDeng BLv RSong J(2024)Candidate Evaluation with Multimodal Data-Driven for RecruitmentPattern Recognition10.1007/978-3-031-78186-5_6(81-96)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-3-031-78186-5_6
Levy AChalamish MShalom BSharir GPeltzman OSalzmann S(2024)A Multi-clustering Unbiased Relative Prediction Recommendation Scheme for Data with Hidden Multiple OverlapsIntelligent Computing10.1007/978-3-031-62277-9_17(284-302)Online publication date: 13-Jun-2024
https://doi.org/10.1007/978-3-031-62277-9_17
Wu SU LKarras P(2023)k-Best Egalitarian Stable Marriages for Task AssignmentProceedings of the VLDB Endowment10.14778/3611479.361152216:11(3240-3252)Online publication date: 1-Jul-2023
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