E-Commerce Product Categorization via Machine Translation
Liling Tan, Maggie Yundi Li, Stanley Kok
Abstract
E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation . In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.