摘要
【目的】总结国内外科学数据推荐的研究现状,为促进科学数据共享研究提供理论基础。【文献范围】在CNKI、WOS、Google Scholar中使用“科学数据推荐”“科学数据集推荐”“Scientific data recommendation”“Scientific dataset recommendation”等关键词进行检索,并结合主题筛选和追溯法,筛选出71篇代表性文献。【方法】基于文献调研与归纳总结方法,分别从推荐模型、结果评价、未来展望三方面对相关研究进行综述与评述。【结果】科学数据推荐对于促进数据共享至关重要。已有研究可分为基于内容过滤、基于协同过滤、基于图模型和基于混合过滤的科学数据推荐。然而,现有研究缺乏对科学数据多源异构信息的综合利用,以及用户隐私保护的相关研究。此外,在可解释性研究和推荐结果的评测方面也存在不足。【局限】由于科学数据类型存在多样性,并未将所有研究逐一列出。【结论】融合多源异构信息的推荐、推荐可解释性、用户隐私保护以及推荐效果评测将是科学数据推荐领域的未来研究方向。
[Objective]This study provides a comprehensive overview of recommendations for scientific data,with the aim of establishing a theoretical basis for the sharing of scientific data.[Coverage]A search was conducted in the CNKI,Web of Science(WOS)and Google Scholar using the keywords such as“scientific data recommendation”and“scientific dataset recommendation”.A total of 71 key articles were identified through thematic and snowball searches.[Methods]A systematic literature review and synthesis approach was used to evaluate existing research.This study provides a comprehensive overview and critical analysis of recommendation models,evaluation metrics and future perspectives.[Results]Recommendations for scientific datasets have been found to play a critical role in facilitating their sharing.Prevalent methods include content filtering,collaborative filtering,graph models,and hybrid filtering.Identified research gaps include the synthesis of multi-source,heterogeneous data,the protection of user privacy,the development of explainable systems,and the evaluation of recommendations.[Limitations]This paper provides an overview of the latest research in this field,focusing on key studies.Due to the inherent diversity of scientific data types,it is not feasible to enumerate every individual study.[Conclusions]Future research directions are identified as integrating heterogeneous information from multiple sources,improving the explainability of recommendations,ensuring privacy protection and refining evaluation methods.
作者
张博睿
杨宁
张鑫
文奕
Zhang Borui;Yang Ning;Zhang Xin;Wen Yi(National Science Library(Chengdu),Chinese Academy of Sciences,Chengdu 610299,China;Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《数据分析与知识发现》
北大核心
2025年第6期1-20,共20页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金项目(项目编号:23XTQ006)的研究成果之一。
关键词
科学数据推荐
数据共享
数据要素
推荐算法
Scientific Data Recommendation
Data Sharing
Data Elements
Recommendation Algorithm
