论文标题
对抗性验证方法在用户定位自动化系统中的概念漂移问题
Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber
论文作者
论文摘要
在用户定位自动化系统中，输入数据中的概念漂移是主要挑战之一。随着时间的推移，它会在新数据上的模型性能恶化。先前对概念漂移的研究主要是在观察性能下降后大多提出的模型再培训。但是，这种方法是次优的，因为该系统仅在新数据的性能差后才解决该问题。在这里，我们引入了一种对抗性验证方法，以在用户定位自动化系统中概念漂移问题。通过我们的方法，该系统在推断之前检测到新数据中的概念漂移，训练模型，并产生适合新数据的预测。我们表明，我们的方法通过AutoMl3终生机器学习挑战数据以及在Uber的内部用户定位自动化系统Malta有效地解决了概念。
In user targeting automation systems, concept drift in input data is one of the main challenges. It deteriorates model performance on new data over time. Previous research on concept drift mostly proposed model retraining after observing performance decreases. However, this approach is suboptimal because the system fixes the problem only after suffering from poor performance on new data. Here, we introduce an adversarial validation approach to concept drift problems in user targeting automation systems. With our approach, the system detects concept drift in new data before making inference, trains a model, and produces predictions adapted to the new data. We show that our approach addresses concept drift effectively with the AutoML3 Lifelong Machine Learning challenge data as well as in Uber's internal user targeting automation system, MaLTA.
