%0 Journal Article %A QIN Hongwu %A ZHAO Meng %A MA Xiuqin %A ZHAO Dezhi %A YAN Wenying %T Collaborator Recommendation Model Based on Academic Level Clustering %D 2022 %R 10.3778/j.issn.1002-8331.2103-0540 %J Computer Engineering and Applications %P 172-181 %V 58 %N 21 %X In order to solve the problem that the existing collaborator recommendation model does not consider the academic level gap between the target scholar and the recommended scholar, this paper proposes an collaborator recommendation model based on academic level clustering , recommend the most suitable collaborators for the target scholars(fitting collaborator recommendation, FCR). FCR uses the K-means algorithm to cluster the scholars in the cooperative network according to the characteristics of the academic level, and then establishes the cooperative network in the same level category, and uses the Katz indicator in the link prediction algorithm to perform similarity to the nodes in the network, and calculates and extracts the research topics of scholars. Finally, the accessibility of the node in the network, whether the academic level is similar or not, and the similarity of research topics are comprehensively considered, comprehensively calculate their recommendation scores and make Top-N recommendations. Compared with other recommendation models, the proposed model has better performance in accuracy, recall and F1 index, which are 5.3%, 2.5% and 4% higher on average. In addition, the academic level matching between the other models has been improved by 37% on average. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0540