一个超级图形神经网络模型与对比学习用于评级-审查建议
Shuyun Fang1, Junling Wang1, Fukun Chen2
1School of Software and Big Data Technology, Dalian Neusoft University of Information, Dalian 116023, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种用于推系统的新型超级图形神经网络,通过整合审查语义和用户-项目交互来提高准确性. 该模型有效地解决了数据稀疏性,并改善了复杂的用户偏好.
科学领域:
- 推系统
- 图形神经网络
- 机器学习
背景情况:
- 数据稀疏是推系统的一个主要挑战,限制了准确性.
- 传统的图形神经网络 (GNN) 与非欧几里德数据结构发生冲突,阻碍了性能.
- 现有的方法往往无法有效地捕捉异构的交互模式.
研究的目的:
- 为评级审查建议提出超级图形神经网络模型.
- 通过整合多式联络信息,特别是审查和用户与项目的互动,提高推的准确性.
- 解决复杂现实数据模型中的欧几里德嵌入的局限性.
主要方法:
- 实现了双图结构:一个审查意识图和一个用户项目交互图.
- 使用过度图形神经网络架构进行联合高阶特征学习.
- 在超标空间中结合对比学习以利用语义和交互数据.
主要成果:
- 拟议的模型显著提高了对现实数据集的建议准确性.
- 与传统方法相比,在处理数据稀疏性方面表现出优越的性能.
- 有效地避免了嵌入高阶特征学习中常见的扭曲问题.
结论:
- 超级图形神经网络与对比学习为评级审查建议提供了强有力的方法.
- 综合复习语义和用户对象交互,提高了表现能力.
- 该模型为提升推系统性能和准确性提供了可靠的解决方案.
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