The global e-commerce store needs to ensure compliance with various regulations at local, national, and international levels. One business use case is to identify face masks to avoid price gouging during times of high demand. In order to keep billions of items safe and legally compliant, it is important to ensure accurate classifications. Classification revisers aim to enhance classification accuracy by detecting and revising incorrect classifications. In this paper, we introduce this problem, along with appropriate online evaluation metrics for large-scale application scenarios. Our proposed method first learns neural network embedding from item textual features to define similar neighbors, and then simply uses these known classification results from k nearest neighbors to estimate an item’s class assignment. Our experiments demonstrate that it outperforms the state-of-the-art baseline approaches and its robustness to large scales. The proposed method can uniquely revise a significant number of classifications correctly, complementary to a multi-label classification system. The study indicates that our simple yet effective approach empowered by GPU computation is a viable solution of a commercial multi-label-classification revision problem.

In e-commerce, product classification is widely used for various purposes. Misclassifying products can cause compliance issues and hurt the company's reputation. To address this problem, we propose an automated system to proactively detect product misclassifications by overcoming several challenges. A large e-commerce retailer can sell billions of distinct products, on which many thousands of classification tasks are performed. At this massive scale, we need to quickly detect misclassifications under a limited budget. In this talk, we point out these challenges and show how we design our system to handle them. When evaluated on a set of Amazon's product classification data, at an overhead of <10% of the classification cost, our system automatically identified and corrected many misclassifications, which would take a human many thousand years to manually find and 14.6 years to manually review and correct if our system were not used.