Categorizing Products at Scale - Shopify

What this post added

This post details the implementation of a product categorization system at Shopify. It addresses the challenges of classifying billions of products into a large, hierarchical taxonomy (Google Product Taxonomy). The solution involves text featurization using HashingTF and PySpark, opting for simplicity and scalability over more complex methods like Word2Vec. The core modeling approach uses Kesler's Construction to transform multi-class classification into a binary classification problem, allowing for a single Logistic Regression model. This approach scales to thousands of categories, leverages taxonomy structure, reduces computational resources, and maintains simplicity. The inference process uses a greedy traversal of the taxonomy to predict the most relevant category path. The post also touches upon evaluation metrics for hierarchical classification.