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Item categorization is a machine learning task which aims at classifyinge-commerce items, typically represented by textual attributes, to their mostsuitable category from a predefined set of categories. An accurate itemcategorization system is essential for improving both the user experience andthe operational processes of the company. In this work, we focus on itemcategorization settings in which the textual attributes representing items arenoisy and short, and labels (i.e., accurate classification of items intocategories) are not available. In order to cope with such settings, we proposea novel learning framework, Ensembled Transferred Embeddings (ETE), whichrelies on two key ideas: 1) labeling a relatively small sample of the targetdataset, in a semi-automatic process, and 2) leveraging other datasets fromrelated domains or related tasks that are large-scale and labeled, to extract"transferable embeddings". Evaluation of ETE on a large-scale real-worlddataset provided to us by PayPal, shows that it significantly outperformstraditional as well as state-of-the-art item categorization methods.

Categorizing Items with Short and Noisy Descriptions using Ensembled Transferred Embeddings