从数据缺失非随机视角的隐式反中积极未标记的学习
Sichao Wang1, Tianyu Xia2, Lingxiao Yang3
1KUKA Robotics China Co., Ltd., Shanghai 201702, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
这项研究解决了推系统 (RS) 中缺少负面标签的挑战,将其制定为积极无标签 (PU) 学习问题. 提出了一种新的两相调试框架,以提高对未测量的混因子的模型稳定性.
科学领域:
- 机器学习 机器学习
- 推系统是一个推系统.
- 数据科学数据科学数据科学
背景情况:
- 没有明确的负面标签是计算机视觉 (CV),自然语言处理 (NLP) 和推系统 (RS) 的常见问题.
- 在CV,NLP和RS中现有的负样本完成方法经常与失踪非随机 (MNAR) 数据性质和未测量的混因素扎,影响模型的稳定性.
- 这些局限性需要先进的技术来准确预测隐式反场景.
研究的目的:
- 解决推系统 (RSs) 中缺少负面标签的挑战,并提供隐式反.
- 提出一个新的框架,以提高RS模型对未测量的混因素的稳定性.
- 提供现有方法的理论分析,并引入一种更可靠的方法.
主要方法:
- 制定 RS 预测任务,以隐性反作为一个正无标记 (PU) 学习问题.
- 制定一个两阶段的减值框架,包括暴露状态归算和双重可靠的估计器.
- 采用强大的解混方法来减轻未测量的混因素的影响,解决基于倾向的方法的局限性.
主要成果:
- 理论分析表明,当存在未测量的混因素时,现有的基于倾向的方法存在偏差.
- 拟议的双重可靠的估计器与强大的解混相结合,有效地减轻了未测量的混因子的影响.
- 在三个真实世界数据集上进行了广泛的实验,验证了拟议方法的有效性.
结论:
- 拟议的PU学习制定和 debiasing 框架为具有隐式反的推系统提供了一个强大的解决方案.
- 这种新的方法有效地处理缺失的负标签和未测量的混因素,提高模型可靠性.
- 这项工作通过提供更有原则和有效的方法来处理数据偏差,从而推进推系统的最新技术.
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