research-article

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

Authors:

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.

References

[1]

Amr Ahmed and Eric P. Xing. 2012. Timeline: A dynamic hierarchical Dirichlet process model for recovering birth/death and evolution of topics in text stream. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. 20–29.

[2]

Hagai Attias. 2000. A variational baysian framework for graphical models. In Advances in Neural Information Processing Systems, S. Solla, T. Leen, and K. Müller (Eds.) 209–215.

[3]

Matthew James Beal. 2003. Variational Algorithms for Approximate Bayesian Inference. University of London, London.

[4]

Josh Bersin. 2013. Corporate Recruiting Explodes: A New Breed of Service Providers. Retrieved on from https://www.forbes.com/sites/joshbersin/2013/05/23/corporate-recruitment-transformed-new-breed-of-service-providers/.

[5]

Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, and Ji-Rong Wen. 2019. Domain adaptation for person-job fit with transferable deep global match network. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 4812–4822.

[6]

Nils Bjorck, Carla P. Gomes, Bart Selman, and Kilian Q. Weinberger. 2018. Understanding batch normalization. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). 7694–7705.

[7]

David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. 2017. Variational inference: A review for statisticians. Journal of the American Statistical Association112, 518 (2017), 859–877.

[8]

David M. Blei and John D. Lafferty. 2006. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning.ACM, New York, NY, 113–120.

[9]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3 (Mar. 2003), 993–1022. Retrieved from http://dl.acm.org/citation.cfm?id=944919.944937.

[10]

Rosalie P. Chamberlain. 2016. Five steps toward recognizing and mitigating bias in the interview and hiring process. Strategic HR Review 15, 5 (2016), 199–203.

[11]

Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. 2009. Reading tea leaves: How humans interpret topic models. In Proceedings of the 22nd International Conference on Neural Information Processing Systems, Vol. 22. Citeseer, 288–296.

[12]

Yu Cheng, Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok Choudhary, and Songtao Guo. 2013. Jobminer: A real-time system for mining job-related patterns from social media. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1450–1453.

Digital Library

[13]

Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, and Mohan Kankanhalli. 2019. MMALFM: Explainable recommendation by leveraging reviews and images. ACM Transactions on Information Systems 37, 2 (2019), 1–28.

Digital Library

[14]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In Workshop on Deep Learning, December (NIPS’14).

[15]

Mamadou Diaby, Emmanuel Viennet, and Tristan Launay. 2013. Toward the next generation of recruitment tools: An online social network-based job recommender system. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 821–828.

Digital Library

[16]

Lan Du, Wray Buntine, and Huidong Jin. 2012. Modelling sequential text with an adaptive topic model. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 535–545.

Digital Library

[17]

Lan Du, Wray Buntine, Huidong Jin, and Changyou Chen. 2012. Sequential latent dirichlet allocation. Knowledge and Information Systems 31, 3 (2012), 475–503.

Digital Library

[18]

Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 315–323.

[19]

Yoav Goldberg and Omer Levy. 2014. word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv:1402.3722. Retrieved from https://arxiv.org/abs/1402.3722.

[20]

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580. Retrieved from https://arxiv.org/abs/1207.0580.

[21]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.

Digital Library

[22]

Wenxing Hong, Siting Zheng, and Huan Wang. 2013. Dynamic user profile-based job recommender system. In 2013 8th International Conference on Computer Science & Education. IEEE, 1499–1503.

[23]

Bao-Xing Huai, Teng-Fei Bao, Heng-Shu Zhu, and Qi Liu. 2014. Topic modeling approach to named entity linking. Journal of Software 25, 9 (2014), 2076–2087.

[24]

Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. 1999. An introduction to variational methods for graphical models. Machine Learning 37, 2 (1999), 183–233.

Digital Library

[25]

Edgar E. Kausel, Satoris S. Culbertson, and Hector P. Madrid. 2016. Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions. Organizational Behavior and Human Decision Processes 137 (2016), 27–44.

[26]

Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https://arxiv.org/abs/1312.6114.

[27]

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. 2017. Automatic differentiation variational inference. The Journal of Machine Learning Research 18, 1 (2017), 430–474.

Digital Library

[28]

Danielle H. Lee and Peter Brusilovsky. 2007. Fighting information overflow with personalized comprehensive information access: A proactive job recommender. In Third International Conference on Autonomic and Autonomous Systems. IEEE, 21–21.

[29]

Chenliang Li, Shiqian Chen, Jian Xing, Aixin Sun, and Zongyang Ma. 2018. Seed-guided topic model for document filtering and classification. ACM Transactions on Information Systems 37, 1 (2018), 1–37.

Digital Library

[30]

Chenliang Li, Yu Duan, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. 2017. Enhancing topic modeling for short texts with auxiliary word embeddings. ACM Transactions on Information Systems 36, 2 (2017), 1–30.

Digital Library

[31]

Huayu Li, Yong Ge, Hengshu Zhu, Hui Xiong, and Hongke Zhao. 2017. Prospecting the career development of talents: A survival analysis perspective. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 917–925.

Digital Library

[32]

Yingzhen Li and Stephan Mandt. 2018. Disentangled sequential autoencoder. In Proceedings of the 35th International Conference on Machine Learning.

[33]

Hao Lin, Hengshu Zhu, Junjie Wu, Yuan Zuo, Chen Zhu, and Hui Xiong. 2020. Enhancing employer brand evaluation with collaborative topic regression models. ACM Transactions on Information Systems 38, 4 (2020), 1–33.

Digital Library

[34]

Hao Lin, Hengshu Zhu, Yuan Zuo, Chen Zhu, Junjie Wu, and Hui Xiong. 2017. Collaborative company profiling: Insights from an employee’s perspective. In Proceedings of the 31st AAAI Conference on Artificial Intelligence1417–1423.

[35]

Wei Liu, Guoxi Cao, and Jianqin Yin. 2019. Bi-level attention model for sentiment analysis of short texts. IEEE Access 7 (2019), 119813–119822.

[36]

Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 30th International Conference on Machine Learning, Vol. 30. 3.

[37]

Jochen Malinowski, Tobias Keim, Oliver Wendt, and Tim Weitzel. 2006. Matching people and jobs: A bilateral recommendation approach. In Proceedings of the 39th Annual Hawaii International Conference onSystem Sciences, Vol. 6. IEEE, 137c–137c.

Digital Library

[38]

Qingxin Meng, Hengshu Zhu, Keli Xiao, Le Zhang, and Hui Xiong. 2019. A hierarchical career-path-aware neural network for job mobility prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 14–24.

Digital Library

[39]

Yishu Miao, Edward Grefenstette, and Phil Blunsom. 2017. Discovering discrete latent topics with neural variational inference. In Proceedings of the 34th International Conference on Machine Learning. 2410–2419.

[40]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems. 3111–3119.

[41]

David Mimno, Hanna M. Wallach, Jason Naradowsky, David A. Smith, and Andrew McCallum. 2009. Polylingual topic models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 880–889.

[42]

Andriy Mnih and Karol Gregor. 2014. Neural variational inference and learning in belief networks. arXiv:1402.0030. Retrieved from https://arxiv.org/abs/1402.0030.

[43]

Ioannis Paparrizos, B. Barla Cambazoglu, and Aristides Gionis. 2011. Machine learned job recommendation. In Proceedings of the 5th ACM Conference on Recommender Systems. ACM, 325–328.

Digital Library

[44]

Shinjee Pyo, Eunhui Kim, et al. 2015. Lda-based unified topic modeling for similar tv user grouping and tv program recommendation. IEEE Transactions on Cybernetics 45, 8 (2015), 1476–1490.

[45]

Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, and Hui Xiong. 2018. Enhancing person-job fit for talent recruitment: An ability-aware neural network approach. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 25–34.

Digital Library

[46]

Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Chao Ma, Enhong Chen, and Hui Xiong. 2020. An enhanced neural network approach to person-job fit in talent recruitment. ACM Transactions on Information Systems 38, 2 (2020), 1–33.

Digital Library

[47]

Chuan Qin, Hengshu Zhu, Chen Zhu, Tong Xu, Fuzhen Zhuang, Chao Ma, Jingshuai Zhang, and Hui Xiong. 2019. DuerQuiz: A personalized question recommender system for intelligent job interview. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2165–2173.

Digital Library

[48]

Rajesh Ranganath, Sean Gerrish, and David M. Blei. 2014. Black box variational inference. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics.

[49]

Gbor Rcz, Attila Sali, and Klaus Dieter Schewe. 2016. Semantic Matching Strategies for Job Recruitment: A Comparison of New and Known Approaches. Springer International Publishing. 149–168.

[50]

Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning. 1278–1286.

[51]

Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas Griffiths, Padhraic Smyth, and Mark Steyvers. 2004. The author-topic models from text corpora. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 487–494.

[52]

Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, and Padhraic Smyth. 2012. The author-topic model for authors and documents. arXiv:1207.4169. Retrieved from https://arxiv.org/abs/1207.4169.

[53]

Tomoki Sekiguchi. 2004. Person-organization fit and person-job fit in employee selection: A review of the literature. Osaka Keidai Ronshu 54, 6 (2004), 179–196.

[54]

Dazhong Shen, Chuan Qin, Chao Wang, Hengshu Zhu, Enhong Chen, and Hui Xiong. 2021. Regularizing variational autoencoder with diversity and uncertainty awareness. In Proceedings of the International Joint Conference on Artificial Intelligence.

[55]

Dazhong Shen, Hengshu Zhu, Chen Zhu, Tong Xu, Chao Ma, and Hui Xiong. 2018. A joint learning approach to intelligent job interview assessment. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3542–3548.

[56]

Baoxu Shi, Shan Li, Jae-Won Yang, Mustafa Emre Kazdagli, and Qi He. 2020. Learning to ask screening questions for job postings. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 549–558.

Digital Library

[57]

Bradley Skaggs and Lise Getoor. 2014. Topic modeling for wikipedia link disambiguation. ACM Transactions on Information Systems 32, 3 (2014), 1–24.

Digital Library

[58]

Akash Srivastava and Charles Sutton. 2017. Autoencoding variational inference for topic models. arXiv:1703.01488. Retrieved from https://arxiv.org/abs/1703.01488.

[59]

Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qing He, and Hui Xiong. 2021. Cost-effective and interpretable job skill recommendation with deep reinforcement learning. In Proceedings of the Web Conference 2021.

Digital Library

[60]

Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qi Zhang, Qing He, and Hui Xiong. 2021. Market-oriented job skill valuation with cooperative composition neural network. Nature Communications 12, 1 (2021), 1–12.

[61]

Mingfei Teng, Hengshu Zhu, Chuanren Liu, Chen Zhu, and Hui Xiong. 2019. Exploiting the contagious effect for employee turnover prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 1166–1173.

[62]

Edwin N. Torres and Amy Gregory. 2018. Hiring manager’s evaluations of asynchronous video interviews: The role of candidate competencies, aesthetics, and resume placement. International Journal of Hospitality Management 75 (2018), 86–93.

[63]

Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 448–456.

[64]

Chong Wang and David M. Blei. 2013. Variational inference in nonconjugate models. Journal of Machine Learning Research 14, Apr (2013), 1005–1031.

[65]

Chao Wang, Hengshu Zhu, Qiming Hao, Keli Xiao, and Hui Xiong. 2021. Variable interval time sequence modeling for career trajectory prediction: Deep collaborative perspective. In Proceedings of the Web Conference 2021.

Digital Library

[66]

Xuerui Wang and Andrew McCallum. 2006. Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 424–433.

Digital Library

[67]

Huang Xu, Zhiwen Yu, Jingyuan Yang, Hui Xiong, and Hengshu Zhu. 2016. Talent circle detection in job transition networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 655–664.

Digital Library

[68]

Tong Xu, Hengshu Zhu, Chen Zhu, Pan Li, and Hui Xiong. 2018. Measuring the popularity of job skills in recruitment market: A multi-criteria approach. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence

[69]

Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Xiaofang Zhou. 2015. Dynamic user modeling in social media systems. ACM Transactions on Information Systems 33, 3 (2015), 1–44.

Digital Library

[70]

Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R Lyu, and Irwin King. 2018. Topic memory networks for short text classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 3120–3131.

[71]

Le Zhang, Tong Xu, Hengshu Zhu, Chuan Qin, Qingxin Meng, Hui Xiong, and Enhong Chen. 2020. Large-scale talent flow embedding for company competitive analysis. In Proceedings of the Web Conference 2020. 2354–2364.

Digital Library

[72]

Le Zhang, Ding Zhou, Hengshu Zhu, Tong Xu, Rui Zha, Enhong Chen, and Hui Xiong. 2021. Attentive heterogeneous graph embedding for job mobility prediction. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Digital Library

[73]

Qi Zhang, Hengshu Zhu, Ying Sun, Hao Liu, Fuzhen Zhuang, and Hui Xiong. 2021. Talent demand forecasting with attentive neural sequential model. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Digital Library

[74]

Yingya Zhang, Cheng Yang, and Zhixiang Niu. 2014. A research of job recommendation system based on collaborative filtering. In 2014 7th International Symposium onComputational Intelligence and Design, Vol. 1. IEEE, 533–538.

Digital Library

[75]

Wayne Xin Zhao, Wenhui Zhang, Yulan He, Xing Xie, and Ji-Rong Wen. 2018. Automatically learning topics and difficulty levels of problems in online judge systems. ACM Transactions on Information Systems 36, 3 (2018), 1–33.

Digital Library

[76]

Chen Zhu, Hengshu Zhu, Hui Xiong, Pengliang Ding, and Fang Xie. 2016. Recruitment market trend analysis with sequential latent variable models. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 383–392.

Digital Library

[77]

Chen Zhu, Hengshu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, and Pan Li. 2018. Person-job fit: Adapting the right talent for the right job with joint representation learning. ACM Transactions on Management Information Systems 9, 3 (2018), 1–17.

Digital Library

[78]

Qile Zhu, Wei Bi, Xiaojiang Liu, Xiyao Ma, Xiaolin Li, and D. Wu. 2020. A batch normalized inference network keeps the KL vanishing away. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

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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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https://dl.acm.org/doi/10.1145/3651158
Jiang FQin CZhang JYao KChen XShen DZhu CZhu HXiong H(2024)Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687439(1-6)Online publication date: 15-Jul-2024
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https://dl.acm.org/doi/10.1016/j.engappai.2023.107306
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Shen DZhu HXiao KZhang XXiong H(2023)Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian LearningACM Transactions on Management Information Systems10.1145/360787514:3(1-20)Online publication date: 12-Sep-2023
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