Discovering Cross-disciplinary Talent Teams Based on Nonnegative Matrix Tri-Factorization

Yu Pan; Pei Wang; Jingbo Huang; Zheng Zeng; Feng Yao

doi:10.2991/978-94-6463-736-6_20

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Discovering Cross-disciplinary Talent Teams Based on Nonnegative Matrix Tri-Factorization

Authors

Yu Pan¹, Pei Wang¹, Jingbo Huang¹, Zheng Zeng², Feng Yao¹^{, *}

¹National University of Defense Technology, Changsha, China

²Academy of Military Sciences, Beijing, China

^*Corresponding author.

Corresponding Author

Feng Yao

Available Online 22 May 2025.

DOI: 10.2991/978-94-6463-736-6_20 How to use a DOI?
Keywords: Cross-disciplinary talent team discovery; Nonnegative matrix tri-factorization; Human resource management
Abstract: The importance of interdisciplinary talent teams is becoming increasingly prominent. Talents of these teams often have multiple identities and belong to several teams simultaneously. In this paper, we employ the methods and theories of complex networks to discover cross-disciplinary talent teams. Firstly, we proposed a unified nonnegative matrix tri-factorization framework, in which the talent association information and research direction information are jointly utilized for collaborative learning and effective integration. Moreover, the talent team relationship matrix is introduced to capture and quantify the tightness of the association between the talent teams, and regularization constraints are applied to ensure the consistency of relationships among talent teams. Finally, a strategy for dividing multidisciplinary and intersecting talent teams is designed, enabling the precise discovery of multidisciplinary talent teams.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2025 4th International Conference on Engineering Management and Information Science (EMIS 2025)
Series: Advances in Computer Science Research
Publication Date: 22 May 2025
ISBN: 978-94-6463-736-6
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-736-6_20 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yu Pan
AU  - Pei Wang
AU  - Jingbo Huang
AU  - Zheng Zeng
AU  - Feng Yao
PY  - 2025
DA  - 2025/05/22
TI  - Discovering Cross-disciplinary Talent Teams Based on Nonnegative Matrix Tri-Factorization
BT  - Proceedings of the 2025 4th International Conference on Engineering Management and Information Science (EMIS 2025)
PB  - Atlantis Press
SP  - 167
EP  - 174
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-736-6_20
DO  - 10.2991/978-94-6463-736-6_20
ID  - Pan2025
ER  -

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