Uber用户定向自动化系统中概念漂移问题的对抗验证方法
Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber
摘要：
在用户定向自动化系统中，输入数据中的概念漂移是主要挑战之一。随着时间的推移，它会降低新数据的模型性能。以往对概念漂移的研究大多是在观察到性能下降后进行模型再训练。然而，这种方法是次优的，因为系统仅在新数据上表现不佳之后才修复问题。在这里，我们介绍了一个对抗性的验证方法，在用户定位自动化系统的概念漂移问题。通过我们的方法，系统在进行推断之前检测新数据中的概念漂移，训练模型，并产生适应新数据的预测。我们表明，我们的方法有效地解决了概念漂移与AutoML3终身机器学习挑战数据，以及在优步的内部用户定位自动化系统，MaLTA。
英文摘要：
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.
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