How We Built a Multi-Task Canonical Ranker for Recommendations at Etsy

Etsy employs recommendation modules to present relevant items to users, each powered by a ranker that scores candidate items for relevance. Traditionally, Etsy used module-specific rankers, but this approach became unwieldy as the number of modules grew. To address this, Etsy developed canonical rankers, which are trained to power multiple modules, ensuring efficiency and consistency. The first canonical ranker focused on visit frequency, using favoriting rate as a surrogate for revisits. The frequency ranker's model structure included a shared-bottom architecture with separate layers for favoriting and purchasing predictions, combined into a final ranking score. The ranker also incorporated a module name feature and balanced training data across modules to ensure generalizability. Despite training on data from a limited subset of modules, the canonical ranker outperformed module-specific rankers on modules not used for training, demonstrating its effectiveness as a canonical solution. The frequency ranker improved favorite rates on both item page and homepage modules, with significant improvements in purchase metrics and other engagement indicators. Since its launch, Etsy has deployed the canonical ranker on multiple modules across web and app platforms. Moving forward, Etsy plans to iterate on the frequency ranker, incorporating more context and exploring novel architectures. The canonical ranker represents a shift in Etsy's recommendation strategy, providing more personalized recommendations and a consistent user experience across platforms and modules.