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相关概念视频

Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
213
Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
452
Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Neural Circuits01:25

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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...
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Sequence Networks of Rotating Machines01:24

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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

Updated: Jul 24, 2025

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图表神经网络引导的对比学习用于顺序推.

Xing-Yao Yang1, Feng Xu1, Jiong Yu1

  • 1School of Software, Xinjiang University, 666, Shengli Road, Urumqi 830049, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

图形神经网络引导的对比学习 (GC4SRec) 通过在增强数据视图中确保语义相似性来增强顺序推. 这种方法有效地减轻了数据稀疏性,并改善了推绩效指标.

关键词:
相反的学习学习学习.图形神经网络的神经网络导向图形神经网络指导的神经网络顺序性推是指一个顺序性的推.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 推系统是一个推系统.

背景情况:

  • 序列推系统通常使用对比学习来解决通过增加用户序列来解决数据稀疏性.
  • 一个关键的限制是增强数据视图中的语义相似性的潜在损失,阻碍了性能.
  • 现有的方法缺乏可靠的机制,以确保语义连贯性在数据增强过程中顺序推.

研究的目的:

  • 为序列推 (GC4SRec) 提出一个新的图形神经网络引导的对比学习框架.
  • 增强对比学习中增强数据视图的语义一致性,以进行顺序推.
  • 为了提高整体性能,并减轻数据稀疏性在连续推任务.

主要方法:

  • 利用图形神经网络 (GNN) 来获得有意义的用户嵌入.
  • 包含一个编码器来计算指导数据增强的项目重要性得分.
  • 开发了一种对比的视图构建方法,利用项目重要性得分和各种增强技术.

主要成果:

  • 在三个公共数据集上,GC4SRec表现出了显著的改进.
  • 该模型实现了1.4%的命中率增加和1.7%的正常化折扣累积收益 (NDCG) 增加.
  • 实验结果验证了建议的指导对比学习方法的有效性.

结论:

  • 图形神经网络引导的对比学习 (GC4SRec) 有效地解决了顺序推中的语义相似性挑战.
  • 拟议的方法通过改善关键指标,如命中率和NDCG,来提高推的性能.
  • 在顺序推系统中,GC4SRec为缓解数据稀疏性问题提供了一个有希望的解决方案.