一个数据增强模型,整合监督和无监督学习进行推
Jiaying Chen1, Zhongrui Zhu2, Haoyang Li1
1School of Software, Xinjiang University, Ürümqi, 830091, People's Republic of China.
Scientific reports
|February 9, 2025
概括
这项研究介绍了DARec,这是一个新的推模型,使用数据增强来克服稀疏标签. DARec有效地从未标记的数据中学习表示,提高了推的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 在图形神经网络 (GNN) 中进行推的监督学习受到极其稀疏的标签数据的影响,限制了嵌入质量.
- 标记数据不足导致推模型过度拟合.
- 现有的数据增强方法通常依赖于传统的标记数据.
研究的目的:
- 提出一个新的推模型,DARec,以解决图形神经网络中稀疏标签的挑战.
- 将监督和无监督学习任务与先进的数据增强技术融合在一起,以提高推性能.
- 有效利用未标记的数据,提高推系统的学习效率.
主要方法:
- 提出了DARec,这是一个结合监督和无监督学习任务的推模型.
- 在监督学习任务中利用扩散模型进行数据增强.
- 在用户项目交互图表和无监督学习的知识图表 (KG) 上,员工的边缘退出.
- 从输入数据生成监管信号,消除对传统标记数据的依赖.
主要成果:
- 与最先进的推模型相比,DARec在三个公共数据集上表现出更高的性能.
- 该模型成功地学习了没有明确标签的特征表示,从而提高了学习效率.
- 在学习过程中尽量减少对原始交互矩阵和图形结构的损坏.
结论:
- 通过有效利用数据增强,DARec为面对稀疏标签数据的推系统提供了强大的解决方案.
- 拟议的方法通过通过自我生成的监管信号利用未标记的数据来提高学习效率.
- 在为推模型开发高质量的嵌入式表示方式方面,DARec 是一个显著的进步.
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