# Pinterest > Visual discovery platform that helps people find, save, organize, and share ideas and inspiration. ## Articles ### [Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest](https://yomu.fyi/post/pinner-progression-better-use-case-representation-driving-weekly-activ.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: Jul 27, 2026 Pinterest designed the Pinner Progression initiative to shift its home feed recommendation pipeline from short-term engagement optimization toward long-term user retention. Standard ranking systems model user actions as immediate sequential features, failing to capture the evolving lifecycles of distinct user habits. To address this gap, the platform introduced User Interest Clusters (UICs), which cluster a user's engaged Pins in OmniSage embedding space using dynamic cluster counts and stateful lifecycle metadata. Pinterest integrated a UIC-aware penalty directly into Sliding Spectrum Diversity (SSD) scoring by matching Pin embeddings to cluster medoids via cosine similarity. Online experiments demonstrated that balancing use-case representation increased interaction diversity, extended session lengths, and delivered meaningful engagement gains across the platform. ### [Automated Schema Evolution in Pinterest’s Next-Generation DB Ingestion Framework](https://yomu.fyi/post/automated-schema-evolution-in-pinterest-s-next-generation-db-ingestion.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: Jun 24, 2026 Pinterest's CDC ingestion platform relies on a multi-stage pipeline built with Kafka, Flink, Spark, and Iceberg, where upstream schema updates risk disrupting data transformations and offline storage. To prevent pipeline failures and schema drift, Pinterest introduced an automated schema evolution framework based on a staged convergence model. Upstream changes are detected via push-based DDL messages and daily pull-based validation checks. The workflow automatically restricts automated updates to additive changes and numeric precision widening, updating Iceberg table definitions and regenerating Flink and Spark code through auditable pull requests. By decoupling schema divergence, code deployment, and data convergence, the system maintains continuous ingestion availability while working toward eventual schema consistency across all storage layers. ### [Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use](https://yomu.fyi/post/making-user-sequence-data-more-cost-efficient-faster-and-easier-to-use.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: May 21, 2026 User sequences serve as core primitives powering machine learning models across ranking, retrieval, and recommendation surfaces at Pinterest. Managing these sequences previously caused high infrastructure costs, operational fragility, and data drift between offline training pipelines and online serving systems. In response, engineering teams redesigned the platform around a single definition executed across multiple runtimes, including real-time streaming, batch backfills, and online inference. The updated architecture pairs a unified enrichment engine with columnar time-partitioned storage and standardized validation tiers. Consequently, the redesign achieved lower storage and network costs, faster signal onboarding via configuration, and improved engagement metrics across major recommendation surfaces. ### [Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models](https://yomu.fyi/post/enhancing-ad-relevance-integrating-real-time-context-into-sequential-r.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: May 8, 2026 Pinterest's sequential ads candidate generator inferred user embeddings offline from historical offsite conversions, which lacked real-time browsing context. Consequently, less than 1% of impressions on the Related Pins surface were attributed to this generator because its retrieved candidates struggled in downstream ranking. To resolve this limitation, engineers developed the Contextual Sequential Two Tower Model, integrating a context layer directly into the query tower alongside user demographic features. Offline training utilized synthetic context derived from positive conversion labels paired with high dropout rates to preserve sequence reliance, while serving split into offline Transformer inference and online context computation. Offline evaluations showed a 3x to 10x increase in Recall@K, and production deployment lifted median candidate relevance by 275% to 300% and overall ROAS by approximately 0.7%. ### [Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer](https://yomu.fyi/post/optimizing-ml-workload-network-efficiency-part-i-feature-trimmer.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: May 1, 2026 Pinterest deployed a root-leaf architecture for online machine learning serving to decouple CPU-based feature preprocessing from GPU-based inference. Because the root cluster retrieved a union of features across models and fanned out the full set to every leaf, network bandwidth became a severe bottleneck. After initial tests with fbthrift LZ4 compression yielded a 20% bandwidth reduction at the cost of CPU and latency overhead, the team implemented Feature Trimmer. This mechanism uses exported model signatures as the source of truth to prune unused features on the root before transmission. By dispatching only required features per model, the system reduced network bandwidth, decreased latency, and enabled cluster rightsizing that saved over $4M annually. ### [From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest](https://yomu.fyi/post/from-clicks-to-conversions-architecting-shopping-conversion-candidate.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: Apr 27, 2026 Pinterest built a dedicated shopping candidate generation model to optimize for lower-funnel purchase conversions rather than relying strictly on engagement retrieval models. Because offsite conversion events are sparse and delayed, engineers trained a single multi-surface two-tower model using conversion data supplemented by duration-weighted click engagement and unengaged ad impressions as hard negatives. The two-tower retrieval architecture incorporates a parallel DCN v2 and MLP layer structure that allows simultaneous learning from the same inputs without information bottlenecks. Later revisions transitioned the system from a multi-head loss structure to a unified single-head multi-task architecture paired with an advertiser-level objective. Deployed across more than 600 million monthly active users, the system increased shopping conversion volume by 2.3% and boosted return on ad spend by 3.1% for United States shopping campaigns.