research-article

Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment

Authors:

Enhong Chen, and

Hui XiongAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 1

Article No.: 15, Pages 1 - 36

https://doi.org/10.1145/3469654

Published: 08 September 2021 Publication History

Abstract

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this article, we propose three novel approaches to intelligent job interview by learning the large-scale real-world interview data. Specifically, we first develop a preliminary model, named Joint Learning Model on Interview Assessment (JLMIA), to mine the relationship among job description, candidate resume, and interview assessment. Then, we further design an enhanced model, named Neural-JLMIA, to improve the representative capability by applying neural variance inference. Last, we propose to refine JLMIA with Refined-JLMIA (R-JLMIA) by modeling individual characteristics for each collection, i.e., disentangling the core competences from resume and capturing the evolution of the semantic topics over different interview rounds. As a result, our approaches can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history. In addition, we exploit our approaches for two real-world applications, i.e., person-job fit and skill recommendation for interview assessment. Extensive experiments conducted on real-world data clearly validate the effectiveness of our models, which can lead to substantially less bias in job interviews and provide an interpretable understanding of job interview assessment.

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Cited By

Liu JWang TYao FPedrycz WSong YHe R(2024)Assessing growth potential of careers with occupational mobility network and ensemble frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107306127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107306
Hu XCheng YZheng ZWang YChi XZhu HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online RecruitmentProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599783(4146-4155)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599783

Index Terms

Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 1

January 2022

599 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3483337

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 08 September 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 1

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Cited By

Liu JWang TYao FPedrycz WSong YHe R(2024)Assessing growth potential of careers with occupational mobility network and ensemble frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107306127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107306
Hu XCheng YZheng ZWang YChi XZhu HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online RecruitmentProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599783(4146-4155)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599783

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