Ling Jiang
ABSTRACT
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.
- Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta. 2018. To trust or not to trust a classifier. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS'18). 5546--5557.Google Scholar
- Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, and Inderjit S. Dhillon. 2022. PECOS: Prediction for enormous and correlated output spaces. Journal of Machine Learning Research, 23(98):1--32.Google Scholar
Index Terms
- Proactive and Automatic Detection of Product Misclassifications at Massive Scale
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