  Product categorization using text data for eCommerce is a very challenging
extreme classification problem with several thousands of classes and several
millions of products to classify. Even though multi-class text classification
is a well studied problem both in academia and industry, most approaches either
deal with treating product content as a single pile of text, or only consider a
few product attributes for modelling purposes. Given the variety of products
sold on popular eCommerce platforms, it is hard to consider all available
product attributes as part of the modeling exercise, considering that products
possess their own unique set of attributes based on category. In this paper, we
compare hierarchical models to flat models and show that in specific cases,
flat models perform better. We explore two Deep Learning based models that
extract features from individual pieces of unstructured data from each product
and then combine them to create a product signature. We also propose a novel
idea of using structured attributes and their values together in an
unstructured fashion along with convolutional filters such that the ordering of
the attributes and the differing attributes by product categories no longer
becomes a modelling challenge. This approach is also more robust to the
presence of faulty product attribute names and values and can elegantly
generalize to use both closed list and open list attributes.
