Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest

This post details the introduction and implementation of User Interest Clusters (UICs) as a novel stateful interest representation for Pinterest's recommendation system. It describes the motivation for moving beyond engagement-only optimization to retention, the limitations of existing sequential models, and the design of UICs. Key technical contributions include: 1. Personalized clustering over engaged content only, making clusters semantically coherent to individual users. 2. Dynamic cluster count determination based on coherence thresholds, allowing for a variable number of interests per user. 3. Stateful lifecycle metadata (recency, frequency) for each UIC to reason about interest maturity. The core signal construction mechanism is detailed as Complete Linkage Agglomerative Hierarchical Clustering on OmniSage embeddings, with a worked example illustrating the process. The post also outlines the system-level integration of UICs across the home feed serving stack (retrieval, ranking, blending) to enable use-case-aware recommendations.