基于规范化的图形自动编码器在推算法中的应用.
Chengxin Xie1, Xiumei Wen1,2, Hui Pang1
1College of Information Engineering, Hebei University of Architecture, Zhangjiakou, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究通过将无效化自编码器与图形卷积神经网络集成来增强社交网络推算法. 这种新的方法解决了用户偏好和数据噪声,提高了预测准确度.
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:
- 社交网络推算法至关重要,但与传统方法面临挑战.
- 深度学习,特别是自编码器和图形卷积神经网络 (GCNs),显示出希望,但有局限性.
- 现有的模型在用户偏好和过度平滑深度GCN中扎,并且经常忽视杂的图形数据.
研究的目的:
- 为社交网络开发一个改进的推算法.
- 解决现有的深度学习模型的局限性,包括用户偏好表示和过度平滑.
- 解决基于图表的推系统中噪音数据的问题.
主要方法:
- 提出了一种结合自编码器和GCNs与L1和L2规范化的新型模型.
- 集成的无噪声自动编码器进入图形自动编码器,以处理噪声图形数据.
- 采用正规化技术的线性融合,以平衡用户偏好并减轻过度平滑.
主要成果:
- 拟议的模型有效地解决了用户偏好和过度光滑问题.
- 成功地减轻了图形特征提取中的噪音数据的影响.
- 在四个基准数据集上,在边缘预测任务中取得了显著的改进,收益高达1.4.
结论:
- 这种新的方法为社交网络推提供了更具竞争力和更强大的解决方案.
- 整合无色化技术和规范化可以提高模型性能和数据处理.
- 该模型在提高推准确度方面表现出卓越的能力,特别是在边缘预测方面.
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