数字图书馆论坛2024，Vol.20Issue(12)：77-86,10.DOI:10.3772/j.issn.1673-2286.2024.12.009
基于node2vec模型的多维度科研合作学者推荐研究
Multi-Dimensional Scholars Recommendation in Scientific Research Cooperation Based on node2vec Model
袁永旭 1成韬2
作者信息
- 1. 山西医科大学管理学院,太原 030001||山西医科大学图书馆,太原 030001
- 2. 山西医科大学管理学院,太原 030001
- 折叠
摘要
Abstract
This article proposes a multi-dimensional scholars recommendation method based on graph embedding model to recognize scholars with a high degree of relevance to the research topic and high potential for collaboration.First,the CSSCI database is used as the data source to construct a fully connected network among authors.Keyword similarity,cooperative relationship,citation relationship,and institutional attributes among authors are integrated into weights for inter author connections.Second,the node2vec graph embedding model is used to perform deep learning on the network and obtain the the node coordinate vectors of each author.Finally,comprehensive vector similarity between authors is obtained,so as to complete the recommendation.The results show that the multi-dimensional graph embedding model proposed in this paper can effectively complete the recommendation of cooperative scholars,and can provide a valuable recommendation list for scholars,so as to promote academic cooperation and communication.关键词
科研合作/学者推荐/图嵌入/node2vec/word2vecKey words
Science Collaboration/Scholars Recommendation/Graph Embedding/node2vec/word2vec分类
社会科学引用本文复制引用
袁永旭,成韬..基于node2vec模型的多维度科研合作学者推荐研究[J].数字图书馆论坛,2024,20(12):77-86,10.
