基于学者画像的科研合作者推荐研究

Recommending Research Collaborators Based on Scholar Profiling

【目的】帮助学者快速地找到合适的科研合作者,促进科研产出,增进学术交流。【方法】采用LDA主题模型、PageRank算法、社会网络分析等方法,全面深入挖掘学者的自然属性、兴趣属性、能力属性、社交属性4个维度特征以构建学者画像,并基于学者偏好开展科研合作者推荐。【结果】从CNKI和CSSCI获取图书情报领域14007篇文献、13292条引文数据及11869位作者验证所提模型,最终向目标学者推荐了20名研究兴趣相似及互补的潜在科研合作者。【局限】未能很好地解决冷启动问题,且在学者能力表征方面忽略了不同署名顺序的作者对论文的贡献度,在实证环节数据量选择有限。【结论】所提模型可以有效地向目标学者推荐高权威度、高相关度,且科研生产力和社交关系等多方面特征均高度匹配的潜在科研合作者,具有较好的应用价值。

[Objective]This paper helps scholars quickly find suitable scientific research partners,and then promote research output and enhance academic exchanges.[Methods]First,we explored the four dimensional characteristics of scholars’natural attributes,interest attributes,ability attributes and social attributes with the LDA topic model,PageRank algorithm and social network analysis.Then,we constructed scholars’profiles,and recommended collaborators based on their preferences.[Results]We examined the proposed method with 14007articles,13292 citations and 11869 authors in the field of Library and Information Science from the CNKI and CSSCI databases.A total of 20 potential collaborators with similar and complementary research interests were recommended to the target scholars.[Limitations]More research is needed to address the cold start issue,as well as the contribution of authors in different signing orders of the papers.The data of the empirical study also needs to be expanded.[Conclusions]The proposed model can effectively recommend potential research collaborators for the target scholars,which has good application value.