增加稀疏的行为数据以与模型自生成和混合生成的样本进行用户身份链接
Hongren Huang1, Jianxin Li1, Feihong Lu1
1Beijing Advanced Innovation Center for Big Data and Brain Computing, China; School of Computer Science and Engineering, Beihang University, Beijing, China.
概括
本研究介绍了SGAMDA,一种新的数据增强技术,通过解决数据稀疏性来改善用户身份链接. SGAMDA 增强了行为数据表示,提高了推系统中的预测准确度.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 用户身份链接对于推系统至关重要,它依赖于用户生成的行为数据.
- 数据稀疏性,包括用户行为不足和低频项,对准确的用户建模构成了重大挑战.
- 由于行为数据稀少,现有的方法在表示错误方面扎.
研究的目的:
- 提出和评估SGAMDA (基于模型和混合生成的样本自生成的数据增强),以解决用户身份链接中的数据稀疏性.
- 提高用户身份链接模型的准确性和稳定性.
- 为了改善用户行为数据的表示.
主要方法:
- 开发了两种数据增强策略:使用变量自编码器自生成样本和混合生成样本.
- 实现了SGAMDA,通过解码表示空间样本和混合行为数据来生成合成训练数据.
- 分类用户行为数据以指导基于数据量和低频项目比例的增强策略的应用.
主要成果:
- 在Movies2Books和CDs2Movies数据集上,SGAMDA显著提高了用户身份链接任务的预测准确性.
- 提出的数据增强方法有效地增强了用户行为表示.
- 证明了SGAMDA在缓解数据稀疏性问题的有效性.
结论:
- SGAMDA提供了一种强大的解决方案,用于解决用户身份链接中的数据稀疏性.
- 该方法通过提高培训数据的质量和数量来提高模型性能.
- 这项工作有助于在数据驱动应用程序中更准确,更可靠地识别用户.
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