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User Interests Analysis and Its Application on the Linked Brain Data Platform

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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Abstract

User-centric academic resource service platforms are essential for scientific researchers, since hundreds of new literatures and data sets are produced every single day, and no one can keep him/herself most up-to-date and handle these resources to find, read and understand the most relevant ones completely manually. In this paper, we introduce some user interests analysis methods and apply them to build personalized recommendation services as a user-centric sub system for the Linked Brain Data (LBD) platform, which is an integrated data and knowledge platform for users, especially Neuroscientists and Artificial Intelligence researchers, to explore and better understand the brain and support their research. For interests analysis, we obtain user related data from relatively static data sources (e.g. user profiles maintained by uses), and more dynamic resources (e.g. publications and online social network contents generated by users, which are with chronological information). For recommendation service, we automatically recommend extracted knowledge in the brain association graph and related articles based on the understanding of research interests of the LBD platform users. Through use case studies, we illustrate the importance and potential value of user-centric services for brain and neuroscience related research.

Yi Zeng and Dongsheng Wang contributed equally to the work and serve as co-first authors.

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Notes

  1. 1.

    Linked Brain Data: http://www.linked-brain-data.org/.

  2. 2.

    ORCID: Universal identifier for scientific researchers: http://orcid.org/.

  3. 3.

    The Loop academic social network: http://loop.frontiersin.org/.

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Acknowledgement

This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z151100000915070, Z161100000216124).

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yi Zeng, Dongsheng Wang & Hongyin Zhu

  2. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Yi Zeng, Dongsheng Wang & Hongyin Zhu

Authors
  1. Yi Zeng
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  2. Dongsheng Wang
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  3. Hongyin Zhu
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Corresponding author

Correspondence to Yi Zeng .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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Zeng, Y., Wang, D., Zhu, H. (2016). User Interests Analysis and Its Application on the Linked Brain Data Platform. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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