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E-commerce platforms categorize their products into a multi-level taxonomytree with thousands of leaf categories. Conventional methods for productcategorization are typically based on machine learning classificationalgorithms. These algorithms take product information as input (e.g., titlesand descriptions) to classify a product into a leaf category. In this paper, wepropose a new paradigm based on machine translation. In our approach, wetranslate a product's natural language description into a sequence of tokensrepresenting a root-to-leaf path in a product taxonomy. In our experiments ontwo large real-world datasets, we show that our approach achieves betterpredictive accuracy than a state-of-the-art classification system for productcategorization. In addition, we demonstrate that our machine translation modelscan propose meaningful new paths between previously unconnected nodes in ataxonomy tree, thereby transforming the taxonomy into a directed acyclic graph(DAG). We discuss how the resultant taxonomy DAG promotes user-friendlynavigation, and how it is more adaptable to new products.

Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation