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Reviewed and published by trentmaziarz, March 23, 2026. Discovered and drafted by our automated research pipeline.

Pinterest uses an AI system called OmniSearchSage to power its search results and shopping recommendations. Unlike earlier systems that treated text searches and image searches as separate problems, OmniSearchSage places search queries, Pin images, and product listings into a single shared mathematical space so that a search for "minimalist bedroom" can surface both text-matching articles and visually relevant room photos at the same time. The system serves approximately 300,000 search requests per second..

Details

OmniSearchSage is a multi-task, multi-entity embedding model: it learns a single unified representation space for three types of items — search queries (text), Pins (images + text), and products (catalog items) — so that similar items end up close together regardless of their format. The model was described at The Web Conference (WWW '24) in April 2024. Pinterest reports improvements of more than 8% in search relevance, more than 7% in user engagement, and more than 5% in ad click-through rate following deployment. Additionally, Pinterest uses a large language model distillation pipeline — where a large model (LLaMA-3 8B) teaches a smaller, faster model to score relevance — to further improve search quality with less computational cost.

Products affected

Pinterest searchPinterest shopping searchPinterest Ads

Sources & Evidence

News Article

News ArticleVerified

OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search

arXiv·Apr 2024

https://arxiv.org/abs/2404.16260

Cite this record

Trace Foundation. (2026). Pinterest: Pinterest uses an AI system called OmniSearchSage to power its search results and shopping recommendations. Unlike earlier systems that treated text searches and image searches as separate problems, OmniSearchSage places search queries, Pin images, and product listings into a single shared mathematical space so that a search for "minimalist bedroom" can surface both text-matching articles and visually relevant room photos at the same time. The system serves approximately 300,000 search requests per second. (data as of 2026-03-23) [Data set record]. AI Trace. https://www.aitrace.org/r/practice/e9ecf6b1-10f5-435b-a031-c3a448146222. Accessed October 13, 2026.

Stable link: https://www.aitrace.org/r/practice/e9ecf6b1-10f5-435b-a031-c3a448146222
Data as of: March 23, 2026
Last verified: March 23, 2026

How to cite AI Trace

Other practices by Pinterest

OtherPinterest maintains an internal program called Responsible AI that covers three areas: building inclusive AI features (the skin tone, body type, and hair pattern tools described in Practice 21), testing its AI systems for unfair bias (using internal tools that measure whether models perform equally well across different groups of users), and developing guidelines for the safe use of generative AI. The program includes red teaming — structured attempts to find flaws or harmful outputs in AI systems before they launch.OtherPinterest is testing AI-powered upgrades to its Boards feature that go beyond simple user-organized collections. In October 2025, Pinterest launched an experiment in the U.S. and Canada where the platform automatically generates outfit ideas from a user's saved fashion Pins ("Styled for you"), creates entirely AI-curated boards based on trending styles ("Boards made for you"), and suggests related products based on what a user has already saved ("Make It Yours").OtherPinterest uses AI to identify diverse skin tones, body types, and hair patterns across billions of images on its platform, then uses those signals to ensure that search results and recommendations reflect a broader range of human appearances. Users can filter fashion search results by body type ranges, skin tone ranges, and hair patterns. The body type technology launched in September 2023, with public user filters rolling out in March 2024; skin tone ranges have been available since 2018.

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