How Shopify Uses Recommender Systems to Empower Entrepreneurs - Shopify

This post details the design and implementation of Shopify's recommender system platform, focusing on collaborative filtering (CF) using user-item interactions. It explains how user preferences are represented as vectors and treated as a binary classification problem to predict item relevance. The post highlights the use of linear models like LRec for learning item-item similarity matrices, enabling task-parallel training for scalability and interpretability through model coefficients. It also discusses the importance of performance metrics beyond precision, such as diversity and serendipity, and how user features can enrich the model to address cold-start problems.