通过 Saliency-Guided Mixup 增强因子化机器的使用
IEEE transactions on pattern analysis and machine intelligence
|January 16, 2024
概括
本研究介绍了MixFM和SMFM,这两种新方法可以增强推系统的因子化机器 (FMs). 这些技术通过生成有益的辅助训练数据来提高从稀疏数据的学习,从而提高模型性能.
科学领域:
- 机器学习 机器学习
- 推系统是一个推系统.
- 数据挖掘 数据挖掘
背景情况:
- 分因机 (FM) 对于推系统非常有效,尤其是在数据稀少的情况下.
- 现有的FM很难模拟非互动特征的直接相互作用,限制了它们的表达力.
- 对于FM的当前数据增强通常需要大量的专业知识和资源.
研究的目的:
- 提出新的方法,MixFM和 Saliency 引导的混合因数分解机 (SMFM),以提高因数分解机的性能.
- 为了使FM能够在稀疏的数据中学习非交互特征之间的直接相互作用.
- 提高因子化机器的概括能力和表达力.
主要方法:
- MixFM:通过对现有数据的凸形组合生成辅助训练数据,灵感来自Mixup.
- SMFM:采用突出指导来创建更具信息性的增强数据,解决MixFM的潜在冗余问题.
- 理论分析以证明将一般化误差界限最小化.
主要成果:
- 无论是MixFM还是SMFM,都显著提高了七个不同数据集的因子化机器的性能.
- 提出的方法有效地学习了以前非互动特征的直接相互作用.
- 实验结果证实了MixFM和SMFM在基线方法上的优越性.
结论:
- MixFM和SMFM提供了有效的策略来增强因子化机器,特别是在稀疏数据场景中.
- 增强技术通过允许直接的特征交互来提高模型的表达力和概括性.
- 该研究强调了仔细生成的"中毒"或混合数据的好处,以改善FM变体.
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