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| A Researcher Recommendation Model for Research Teams |
Liu Chengshan1,Li Puguo1( ),Wang Zhen2 |
1School of Economics and Management, Xidian University, Xi’an 710126,China
2Chang’an University Library, Xi’an 710064,China |
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Abstract [Objective] This study proposes a deep learning-based recommendation model for research teams to meet recruitment needs and improve recommendation efficiency. [Methods] Firstly, we applied the self-attention mechanism to learn the semantic representation of teams. Then, we employed the neural collaborative filtering model to study the nonlinear relationship between teams and researchers. Finally, we obtained the degree of fit between teams and individuals as the basis for recommendation. [Results] Compared with the baseline models, the proposed one increased the recommendation accuracy and F1 value by 10.22% and 10.25%, respectively, on public datasets. It performed exceptionally well in real-world recommendation scenarios. [Limitations] The parameter size of the deep learning model is relatively small, leaving room for optimization. [Conclusions] The proposed model can effectively enhance the efficiency of recruiting researchers, helping research service institutions improve their services and satisfy the needs of research teams.
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Received: 10 February 2023
Published: 12 April 2024
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| Fund:Scientific and Technological Innovation 2030-“New Generation Artificial Intelligence” Major Project(2021ZD0113702) |
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Corresponding Authors:
Li Puguo，ORCID：0000-0001-7394-5062， E-mail： 940680952@qq.com。
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