NNCR: Revising classifications using embedding based k-nearest-neighbor search

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, com-plementary 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.