通过推算和社会意识图形卷积神经网络来增强推系统
Azadeh Faroughi1, Parham Moradi2, Mahdi Jalili3
1Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
概括
本研究引入了一种新的方法,通过解决数据稀疏性来增强推系统. 通过将信任和归算图与注意力机制集成,它可以提供更个性化和更有效的内容建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 推系统对于内容发现至关重要.
- 协作过方法分析用户-项目交互,经常面临稀疏数据的挑战.
- 数据稀疏性阻碍了准确和个性化的建议.
研究的目的:
- 开发一种新的方法来缓解推系统中的稀疏性.
- 通过结合各种数据源来提高推的准确性和个性化性.
主要方法:
- 纳入各种数据来源:信任声明和归算图.
- 基于用户项目矩阵和类似用户的平均费率构建一个归算图.
- 使用信任图表来捕捉用户关系和信任水平.
- 应用注意力机制来微调组合图形的影响 (用户项目评级,信任,归纳).
主要成果:
- 与最先进的推器相比,提出的方法显示出更高的性能.
- 在现实世界数据集评估中观察到的一致性超出性能.
- 在推系统中有效缓解稀疏性挑战.
结论:
- 综合方法有效地解决了推系统的稀疏性.
- 该方法在内容建议中提供了增强的个性化和有效性.
- 这项研究为加强推系统性能提供了强有力的解决方案.
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