科学论文推系统使用深度学习和引用网络中的链接预测.
1Dean's Office, Yellow River Conservancy Technical Institute, Kaifeng, 475004, Henan, China.
Heliyon
|August 12, 2024
概括
一个新的推系统 (RECSA) 结合了内容分析和引用网络,以改善科学文章的发现. 这种方法提高了0.9%的精度,并提供了更准确,更有效的建议.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 图书统计学 图书统计学
背景情况:
- 科学出版物的指数增长需要先进的方法来发现文章.
- 传统的基于内容和引用的方法在推相关的科学文章方面存在局限性.
- 有效地导航和发现相关研究是科学家面临的重大挑战.
研究的目的:
- 设计和评估一个新的科学文章推系统 (RECSA).
- 通过整合内容分析和引用网络数据,提高科学文章推的准确性和效率.
- 解决现有方法在处理越来越多的科学文献的局限性.
主要方法:
- 利用自然语言处理和深度学习 (卷积神经网络 - CNN) 进行文章标题的内容分析.
- 使用术语频率反向文档频率 (TF-IDF) 来确定单词的重要性和CNN来减少特征尺寸.
- 通过共弦相似度计算内容相似性,并通过链接预测分析引用网络.
- 结合基于内容和引用的相似度矩阵,使用最终建议的影响系数.
主要成果:
- 整合TF-IDF和CNN用于内容分析,与以前的方法相比,精度至少提高了0.32%.
- 在RECSA系统中,第一个建议的精度达到99.01%,比较方法的最低改进率为0.9%.
- 内容和引用数据的结合方法显著提高了推的准确性和效率.
结论:
- RECSA为推科学文章提供了更准确,更有效的解决方案.
- 混合方法有效地利用了文本内容和引用网络结构.
- 该系统为研究人员在不断扩大的科学文献领域中提供了宝贵的工具.
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