  For e-commerce companies with large product selections, the organization and
grouping of products in meaningful ways is important for creating great
customer shopping experiences and cultivating an authoritative brand image. One
important way of grouping products is to identify a family of product variants,
where the variants are mostly the same with slight and yet distinct differences
(e.g. color or pack size). In this paper, we introduce a novel approach to
identifying product variants. It combines both constrained clustering and
tailored NLP techniques (e.g. extraction of product family name from
unstructured product title and identification of products with similar model
numbers) to achieve superior performance compared with an existing baseline
using a vanilla classification approach. In addition, we design the algorithm
to meet certain business criteria, including meeting high accuracy requirements
on a wide range of categories (e.g. appliances, decor, tools, and building
materials, etc.) as well as prioritizing the interpretability of the model to
make it accessible and understandable to all business partners.
