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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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相关实验视频

动机和超节点增强的封闭图形神经网络,用于基于会话的推.

Ronghua Lin1, Chang Liu2, Hao Zhong1

  • 1School of Computer Science, South China Normal University, Guangzhou, 510631, China; Pazhou Lab, Guangzhou, 510330, China.

Neural networks : the official journal of the International Neural Network Society
|March 27, 2025
PubMed
概括

这项研究引入了一种新的推系统,该系统使用图形神经网络 (GNN) 来更好地预测匿名会话中的用户行为. 增强的系统通过分析项目关系和用户模式来改进建议.

关键词:
图表神经网络的神经网络动机增强的增强动机基于会议的建议建议.超级节点增强的超级节点

相关实验视频

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 基于会话的推系统可以预测用户在短暂的匿名会话中的互动.
  • 现有的图形神经网络 (GNN) 方法忽略了会话图中的微结构和用户行为模式.
  • 用户行为数据通常稀疏且动态,对推准确性构成挑战.

研究的目的:

  • 提出一个新的基于Motif和超节点的增强式会话推系统 (MSERS).
  • 通过结合微结构来解决现有的基于GNN的方法的局限性.
  • 改进对象依赖和用户行为在基于会话的建议中的表示.

主要方法:

  • 构建一个全球会话图,将动机作为超级节点.
  • 识别和编码微结构 (图案),以丰富图形拓.
  • 使用超级节点增强的门式图形神经网络 (GGNN) 来改进会话表示.

主要成果:

  • MSERS有效地捕捉了长期和潜在的项目依赖.
  • 与基线方法相比,拟议的方法显著增强了会话表示.
  • 在真实世界数据集上的实验验证实了MSERS的优越性.

结论:

  • 将微结构和图案纳入超级节点增强了基于GNN的会话推器.
  • MSERS为理解和利用复杂的用户行为模式提供了一个强大的框架.
  • 这些发现为提高基于会话的推系统的准确性和有效性提供了宝贵的见解.