Dynamic Talent Flow Analysis with Deep Sequence Prediction Modeling

Talent flow analysis is a process for analyzing and modeling the flows of employees into and out of targeted organizations, regions, or industries. A clear understanding of talent flows is critical for many applications, such as human resource planning, brain drain monitoring, and future workforce forecasting. However, existing studies on talent flow analysis are either qualitative or limited by coarse level quantitative modeling. To this end, in this paper, we provide a fine-grained data-driven approach to model the dynamics and evolving nature of talent flows by leveraging the rich information available in job transition networks. Specifically, we first investigate how to enrich the sparse talent flow data by exploiting the correlations between the stock price movement and the talent flows of public companies. Then, we formalize the talent flow modeling problem as to predict the increments of the edge weights in the dynamic job transition network. In this way, the problem is transformed into a multi-step time series forecasting problem. A deep sequence prediction model is developed based on the recurrent neural network model, which consumes multiple input sources derived from dynamic job transition networks. Finally, experimental results on real-world data show that the proposed model outperforms other benchmark models in terms of prediction accuracy. The results also indicate that the proposed model can provide reasonable performance even if the historical talent flow data are not completely available.

[1]  Hui Xiong,et al.  Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective , 2018, IJCAI.

[2]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Jing Zhao,et al.  Prediction of Links and Weights in Networks by Reliable Routes , 2015, Scientific Reports.

[5]  Margot Edwards,et al.  Exploring the dynamics of New Zealand's talent flow , 2005 .

[6]  Hui Xiong,et al.  Learning Career Mobility and Human Activity Patterns for Job Change Analysis , 2015, 2015 IEEE International Conference on Data Mining.

[7]  Jia Li,et al.  How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking , 2016, KDD.

[8]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[9]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[10]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[11]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[12]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[13]  Yi Zhang,et al.  Is it time for a career switch? , 2013, WWW.

[14]  Thomas W. H. Ng,et al.  Careers: Mobility, Embeddedness, and Success , 2007 .

[15]  Christopher Kermorvant,et al.  Dropout Improves Recurrent Neural Networks for Handwriting Recognition , 2013, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[16]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Christos Faloutsos,et al.  Edge Weight Prediction in Weighted Signed Networks , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[18]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[19]  Hui Xiong,et al.  Talent Circle Detection in Job Transition Networks , 2016, KDD.

[20]  Allan Schweyer,et al.  Talent Management Systems: Best Practices in Technology Solutions for Recruitment, Retention and Workforce Planning , 2004 .

[21]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  Wei Xu,et al.  Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) , 2014, ICLR.

[24]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[25]  Stuart C. Carr,et al.  From global careers to talent flow: Reinterpreting ‘brain drain’ , 2005 .

[26]  David G. Allen,et al.  Meta-Analytic Review of Employee Turnover as a Predictor of Firm Performance , 2013 .

[27]  David Zweig,et al.  Competing for talent: China's strategies to reverse the brain drain , 2006 .

[28]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[29]  R. Schuler,et al.  Global talent management: Literature review, integrative framework, and suggestions for further research , 2010 .

[30]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[32]  Phil Blunsom,et al.  Recurrent Continuous Translation Models , 2013, EMNLP.

[33]  Anmol Bhasin,et al.  Modeling professional similarity by mining professional career trajectories , 2014, KDD.