一种以科研团队为服务对象的科研人员推荐模型

A Researcher Recommendation Model for Research Teams

【目的】本研究提出一种针对科研团队的深度学习组推荐模型,旨在满足科研团队招聘科研人员的需求,提高推荐效率。【方法】首先应用自注意力机制学习团队的语义表示,接着采用神经协同过滤模型学习团队与科研人员间的非线性关系,最终得到团队与人员的契合程度作为推荐的依据。【结果】实验结果显示,在公共数据集上,与基线模型相比,本文模型在推荐正确率和F1值上分别提高10.22和10.25个百分点,在实际推荐场景中表现优异。【局限】深度学习模型的参数量较小,仍有优化空间。【结论】本文模型可以有效提高科研人员招聘的效率,有助于科研服务机构提升服务水平,满足科研团队招聘人员的需求。

[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.