research-article

Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective

Authors:

Hui XiongAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 612 - 623

https://doi.org/10.1145/3442381.3449959

Published: 03 June 2021 Publication History

Abstract

In today’s fast-evolving job market, the timely and effective understanding of the career trajectories of talents can help them quickly develop necessary skills and make the right career transitions at the right time. However, it is a non-trivial task for developing a successful career trajectory prediction method, which should have the abilities for finding the right timing for job-hopping, identifying the right companies, and matching the right positions for the candidates. While people have been trying to develop solutions for providing some of the above abilities, there is no total solution or complete framework to integrate all these abilities together. To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Along this line, we first exploit a hierarchical deep sequential modeling network for career embedding and extract latent talent factors from multiple networks, which are designed with different functions of handling related issues of the timing, companies, and positions for job-hopping. Then, we perform collaborative filtering for generating personalized predictions. Furthermore, we propose a temporal encoding mechanism to handle dynamic temporal information so that TACTP is capable of generating time-aware predictions by addressing the challenges for variable interval time sequence modeling. Finally, we have conducted extensive experiments on large-scale real-world data to evaluate TACTP against the state-of-the-art baselines, and the results show that TACTP has advantages over baselines on all targeted tasks for career trajectory prediction.

References

[1]

[1] 2017. https://www.forbes.com/sites/joshbersin/2017/05/26/google-for-jobs-potential-to-disrupt-the-200-billion-recruiting-industry/.

[2]

William J Anderson. 2012. Continuous-time Markov chains: An applications-oriented approach. Springer Science & Business Media.

[3]

David Arthur and Sergei Vassilvitskii. 2007. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 1027–1035.

Digital Library

[4]

Wenbo Chen, Pan Zhou, Shaokang Dong, Shimin Gong, Menglan Hu, Kehao Wang, and Dapeng Wu. 2018. Tree-Based Contextual Learning for Online Job or Candidate Recommendation With Big Data Support in Professional Social Networks. IEEE Access 6(2018), 77725–77739.

[5]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014).

[6]

Vachik S Dave, Baichuan Zhang, Mohammad Al Hasan, Khalifeh AlJadda, and Mohammed Korayem. 2018. A combined representation learning approach for better job and skill recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1997–2005.

Digital Library

[7]

Thomas H Davenport, Jeanne Harris, and Jeremy Shapiro. 2010. Competing on talent analytics. Harvard business review 88, 10 (2010), 52–58.

[8]

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1243–1252.

Digital Library

[9]

Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 6645–6649.

[10]

Miao He, Dayong Shen, Yuanyuan Zhu, Renjie He, Tao Wang, and Zhongshan Zhang. 2019. Career Trajectory Prediction based on CNN. In 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE, 22–26.

Digital Library

[11]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, 173–182.

Digital Library

[12]

John J Hopfield. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences 79, 8 (1982), 2554–2558.

[13]

Binbin Jin, Hongke Zhao, Enhong Chen, Qi Liu, and Yong Ge. 2019. Estimating the days to success of campaigns in crowdfunding: A deep survival perspective. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4023–4030.

Digital Library

[14]

Alan Karr. 2017. Point processes and their statistical inference. Routledge.

[15]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.

Digital Library

[16]

John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001).

[17]

Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan. 2019. Predicting Path Failure In Time-Evolving Graphs. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1279–1289.

Digital Library

[18]

Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 322–330.

Digital Library

[19]

Liangyue Li, How Jing, Hanghang Tong, Jaewon Yang, Qi He, and Bee-Chung Chen. 2017. Nemo: Next career move prediction with contextual embedding. In Proceedings of the 26th International Conference on World Wide Web Companion. 505–513.

Digital Library

[20]

Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 305–314.

Digital Library

[21]

Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1734–1743.

Digital Library

[22]

Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, and Prem Natarajan. 2016. Temporal learning and sequence modeling for a job recommender system. In Proceedings of the Recommender Systems Challenge. 1–4.

Digital Library

[23]

Rui Liu, Wenge Rong, Yuanxin Ouyang, and Zhang Xiong. 2017. A hierarchical similarity based job recommendation service framework for university students. Frontiers of Computer Science 11, 5 (2017), 912–922.

Digital Library

[24]

Ye Liu, Luming Zhang, Liqiang Nie, Yan Yan, and David S Rosenblum. 2016. Fortune teller: predicting your career path. In Thirtieth AAAI conference on artificial intelligence.

[25]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579–2605.

[26]

Hongyuan Mei and Jason M Eisner. 2017. The neural hawkes process: A neurally self-modulating multivariate point process. In Advances in Neural Information Processing Systems. 6754–6764.

[27]

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

[28]

Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in neural information processing systems. 1257–1264.

[29]

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

Digital Library

[30]

Bharat Patel, Varun Kakuste, and Magdalini Eirinaki. 2017. CaPaR: a career path recommendation framework. In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 23–30.

[31]

Martin Pavlovski, Jelena Gligorijevic, Ivan Stojkovic, Shubham Agrawal, Shabhareesh Komirishetty, Djordje Gligorijevic, Narayan Bhamidipati, and Zoran Obradovic. 2020. Time-Aware User Embeddings as a Service. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3194–3202.

Digital Library

[32]

Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, and Christian Igel. 2019. U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging. In Advances in Neural Information Processing Systems. 4417–4428.

[33]

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 130–137.

Digital Library

[34]

Narda R Quigley and Walter G Tymon. 2006. Toward an integrated model of intrinsic motivation and career self-management. Career development international(2006).

[35]

Radim Rehurek and Petr Sojka. 2010. Software framework for topic modelling with large corpora. In In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer.

[36]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811–820.

Digital Library

[37]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285–295.

Digital Library

[38]

Walid Shalaby, BahaaEddin AlAila, Mohammed Korayem, Layla Pournajaf, Khalifeh AlJadda, Shannon Quinn, and Wlodek Zadrozny. 2017. Help me find a job: A graph-based approach for job recommendation at scale. In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 1544–1553.

[39]

Shuo Shang, Ruogu Ding, Kai Zheng, Christian S Jensen, Panos Kalnis, and Xiaofang Zhou. 2014. Personalized trajectory matching in spatial networks. The VLDB Journal 23, 3 (2014), 449–468.

Digital Library

[40]

Qingxiong Tan, Mang Ye, Baoyao Yang, Siqi Liu, Andy Jinhua Ma, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, and PongChi Yuen. 2020. DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 930–937.

[41]

Ibraiz Tarique and Randall S Schuler. 2010. Global talent management: Literature review, integrative framework, and suggestions for further research. Journal of world business 45, 2 (2010), 122–133.

[42]

Mingfei Teng, Hengshu Zhu, Chuanren Liu, Chen Zhu, and Hui Xiong. 2019. Exploiting the Contagious Effect for Employee Turnover Prediction. (2019).

[43]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.

[44]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235–1244.

Digital Library

[45]

Pengyang Wang, Yingtong Dou, and Yang Xin. 2016. The analysis and design of the job recommendation model based on GBRT and time factors. In 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA). IEEE, 29–35.

[46]

Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval. 403–412.

Digital Library

[47]

Francisco Wilhelm and Andreas Hirschi. 2019. Career self-management as a key factor for career wellbeing. In Theory, Research and Dynamics of Career Wellbeing. Springer, 117–137.

[48]

Huang Xu, Zhiwen Yu, Hui Xiong, Bin Guo, and Hengshu Zhu. 2015. Learning career mobility and human activity patterns for job change analysis. In 2015 IEEE International Conference on Data Mining. IEEE, 1057–1062.

Digital Library

[49]

Huang Xu, Zhiwen Yu, Jingyuan Yang, Hui Xiong, and Hengshu Zhu. 2018. Dynamic talent flow analysis with deep sequence prediction modeling. IEEE Transactions on Knowledge and Data Engineering 31, 10(2018), 1926–1939.

[50]

Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems. In IJCAI. 3203–3209.

[51]

Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, and Sriraam Natarajan. 2017. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowledge-Based Systems 136 (2017), 37–45.

Digital Library

[52]

Denghui Zhang, Junming Liu, Hengshu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, and Hui Xiong. 2019. Job2Vec: Job title benchmarking with collective multi-view representation learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2763–2771.

Digital Library

[53]

Le Zhang, Hengshu Zhu, Tong Xu, Chen Zhu, Chuan Qin, Hui Xiong, and Enhong Chen. 2019. Large-Scale Talent Flow Forecast with Dynamic Latent Factor Model?. In The World Wide Web Conference. 2312–2322.

Digital Library

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WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

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Google
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Jie Tang
Tsinghua University
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https://dl.acm.org/doi/10.1145/3651158
Zhu YWang CZhang QXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph Signal Diffusion Model for Collaborative FilteringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657759(1380-1390)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657759
Wu WWang CShen DQin CChen LXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657724(1242-1252)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657724
Wang SSui YWang CXiong HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unleashing the Power of Knowledge Graph for Recommendation via Invariant LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645576(3745-3755)Online publication date: 13-May-2024
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