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Dynamic Talent Flow Analysis with Deep Sequence Prediction Modeling - 2018

Dynamic Talent Flow Analysis With Deep Sequence Prediction Modeling

Research Paper on Dynamic Talent Flow Analysis With Deep Sequence Prediction Modeling

Research Area:  Machine Learning


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.

Talent Flow Analysis
Deep Sequence Prediction
Machine Learning
Deep Learning

Author(s) Name:  Huang Xu; Zhiwen Yu; Jingyuan Yang; Hui Xiong and Hengshu Zhu

Journal name:   IEEE Transactions on Knowledge and Data Engineering

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TKDE.2018.2873341

Volume Information:  Volume: 31, Issue: 10, Oct. 1 2019,Page(s): 1926 - 1939