Findme-scholar:使用适应性主题兴趣领域建模增强研究合作的上下文推系统
Ary Mazharuddin Shiddiqi1, Moch Nafkhan Alzamzami1, Ilham Gurat Adillion1
1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
MethodsX
|September 2, 2025
概括
通过模拟不断发展的兴趣来识别潜在的研究合作者. 通过语义分析成功提出了超越现有网络的新连接.
科学领域:
- 计算机科学
- 图书统计学
- 研究信息学
背景情况:
- 找出研究合作者是一项挑战,尤其是在大型的多学科环境中.
- 传统方法通常依赖于静态配置文件和现有网络,限制了发现.
研究的目的:
- 介绍Findme-Scholar,一个背景研究人员推系统.
- 通过模拟不断发展的主题,加强研究合作.
- 提供超越传统方法的上下文意识的建议.
主要方法:
- 开发了一种适应性主题兴趣区域建模方法.
- 分析出版物元数据和语义内容以捕捉不断变化的研究人员兴趣.
- 实施了一个背景推系统 (Findme-Scholar).
主要成果:
- 成功推的研究人员没有先前的共同作者联系.
- 证明了在现有网络之外识别潜在合作者的能力.
- 在捕捉主题和上下文相似性方面表现出有效性.
结论:
- Findme-Scholar有效地模拟了不断变化的研究兴趣,以改善合作.
- 该系统发现了已建立的共同作者网络之外的相关研究人员.
- 语义和元数据分析使得协作者能够进行上下文意识的建议.
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