扩散增强图对比学习为知识意识建议
IEEE transactions on neural networks and learning systems
|December 30, 2025
概括
扩散增强图形对比学习 (CL) 通过使用图形扩散来增强推系统,以避免采样偏差并减轻知识图和用户项目交互图之间的信息不平衡. 新的DAGCL模型显著优于现有方法.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 推系统是一个推系统.
背景情况:
- 知识图 (KG) 对比学习 (CL) 对推系统至关重要.
- 由于随机掩盖,现有的方法存在抽样偏差和解释性问题.
- 在KG和用户项目交互图 (UIG) 之间信息不平衡阻碍了模型的性能.
研究的目的:
- 提出一种新型模型,扩散增强图形CL (DAGCL),以解决当前KG-CL方法的局限性.
- 通过图形扩散机制改进CL中的数据增强.
- 减轻信息不平衡,增强UIG对预测准确性的影响.
主要方法:
- DAGCL使用图形扩散机制来增强数据,确保生成的图形与原来的UIG相似.
- 内图和内图CL (GCL) 已实施,以平衡KG和UIG信息.
- 结构扩散图与信息扩散图集成,以提供全面的扩散表示.
主要成果:
- 拟议的DAGCL模型在三个真实世界数据集中显著优于最先进的模型.
- 图形扩散机制有效地避免了采样偏差,并保留了UIG特征.
- 结合的CL策略成功地减轻了信息不平衡,提高了预测准确度.
结论:
- DAGCL为推系统提供了一个强大的和有效的KG-CL方法.
- 扩散增强策略通过保留基本的交互模式和结构特征来提高模型性能.
- 在推模型中,DAGCL在克服采样噪声和语义稀释方面取得了重大进展.
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