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| A Method Considering Local and Global Information for Constructing Stereoscopic and Accurate Portraits of Scientific Researchers |
| Zhang Yanan, Huang Jingli, Wang Gang |
| School of Management, Hefei University of Technology, Hefei 230009 |
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Abstract By constructing scientific research behavior portraits, researchers can easily use various research services for efficiency. Existing research often abstracts the portrait problem into a multi-classification problem without considering the full use of information and the problem of updating portraits. Accordingly, this study proposes a scientific research behavioral portrait method for researchers considering local and global information and introduces a deep learning method. Deep learning can extract highly abstract features for sequence modeling, extracting partial portraits, and combining global information to build stereoscopic and accurate portraits. Finally, based on the actual scientific research behavior data, the method proposed in this study is verified, and its effectiveness is proven.
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Received: 24 April 2019
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