A Researcher Recommendation Model for Research Teams

[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. © 2024 Chinese Academy of Sciences. All rights reserved.