基于学术网络的跨学科论文推荐研究  

Research on Interdisciplinary Paper Recommendation Based on Academic Network

作　　者：杜瑾 熊回香[1] 向瀛泓 Du Jin;Xiong Huixiang;Xiang Yinghong(School of Information Management,Central China Normal University,Wuhan 430079)

机构地区：[1]华中师范大学信息管理学院,武汉430079

出　　处：《情报学报》2024年第5期516-527,共12页Journal of the China Society for Scientific and Technical Information

基　　金：国家社会科学基金项目“融合知识图谱和深度学习的在线学术资源挖掘与推荐研究”(19BTQ005)。

摘　　要：为满足情报学科研人员对跨学科论文的需求,本文构建了基于学术网络的跨学科论文推荐模型。首先,根据论文关键词耦合网络及作者对论文的引用网络特征,挖掘作者与论文的相关性,实现基于关键词耦合的论文推荐;其次,利用作者引文耦合网络特征及作者跨学科引用关系、论文共被引关系与论文的学科属性,分别挖掘作者与论文的跨学科性,并计算跨学科性相似度,实现基于学科相似性的论文推荐;最后,结合基于关键词耦合的论文推荐和基于学科相似性的论文推荐,实现跨学科论文混合推荐。以CSSCI(Chinese Social Sciences Citation Index)数据库中的数据对模型进行验证,实证结果表明,本文提出的推荐模型推荐结果具备跨学科性;与基于关键词耦合的论文推荐方法相比,结合跨学科特征后在作者推荐成功率、平均准确率和平均召回率上均有提高。To meet the needs of information science researchers for interdisciplinary papers,this study develops an inter-disciplinary paper recommendation model based on academic network.First,according to the keyword coupling network and author citation network characteristics,the correlation between the author and the paper is extracted,facilitating paper recommendation based on keyword coupling.Second,this model utilizes the author citation coupling network,encompass-ing cross-disciplinary citation relationships,co-citation relationships,and the subject attribute of the paper.This informa-tion is used to mine interdisciplinarity aspects of authors and papers,calculating interdisciplinary similarity for subject-based recommendations.Finally,the study integrates recommendations based on keyword coupling and subject similarity to achieve a hybrid recommendation for interdisciplinary papers.The model is validated using data from Chinese Social Sciences Citation Index(CSSCI)database.Empirical results demonstrate that the recommended papers exhibit interdisci-plinary characteristics.Compared with keyword coupling-based recommendations,combining interdisciplinary characteris-tics improves author recommendation success rates,average accuracy rates,and average recall rates.

关 键 词：情报学 跨学科 学术网络 论文推荐 

分 类 号：G353.1[文化科学—情报学]