# pinner-experience

Published articles for pinner-experience.

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## Scaling Conditional Learned Retrieval for Pinterest Home Feed

DevFeed: [Scaling Conditional Learned Retrieval for Pinterest Home Feed](<https://devfeed.tech/articles/scaling-conditional-learned-retrieval-for-pinterest-home-feed-1233.md>)

Original publisher: [Read original article](<https://medium.com/pinterest-engineering/scaling-conditional-learned-retrieval-for-pinterest-home-feed-ecfba7e5a426?source=rss----4c5a5f6279b6---4>)

Author: Pinterest Engineering

Published: 2026-08-26T14:01:05Z

Content type: article

Language: en

Sources: [Pinterest Engineering Blog - Medium](<https://devfeed.tech/sources/pinterest-engineering-blog-medium.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [eng-culture](<https://devfeed.tech/tags/eng-culture.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [pinner-experience](<https://devfeed.tech/tags/pinner-experience.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>)

### AI overview

Pinterest describes scaling Conditional Learned Retrieval for home-feed candidate generation. The approach extends two-tower retrieval with explicit contexts that produce multiple user embeddings for different interests and retrieval use cases.

### Source excerpt

Devin Kreuzer | Sr. Machine Learning Engineer; Yichi Wang | Machine Learning Engineer I; Sujan Reddy Ale | Machine Learning Engineer I; Zelun Wang | Sr. Machine Learning Engineer; Hongtao Lin | Sr. Machine Learning Engineer; Piyush Maheshwari | Staff Machine Learning Engineer Pinterest home feed candidate generation is a large-scale User-to-Pin retrieval problem. A common approach is a two-tower model: a user tower encodes the user, an item tower encodes candidate Pins, and approximate nearest neighbor search retrieves Pins close to the user embedding. But Pinterest users often have multiple intentions at once -- planning a renovation, saving recipes, exploring fashion, or organizing travel ideas. A single retrieval embedding can struggle to capture this diversity. Conditional Learned Retrieval, or CLR, extends the two-tower setup by conditioning the user tower on an explicit retrieval context. Instead of producing only one user embedding, CLR can generate condition-aware embeddings that reflect different aspects of a user's interests while still grounding retrieval in the user's overall behavior. Prior Pinterest work studied this formulation in two settings. The RecSys'24 paper: Bootstrapping Conditional Retrieval for User-to-Item Recommendations described how to bootstrap conditional retrieval by constructing training data for (user, condition) -> item retrieval from existing user-item and condition signals, and applied it to interest-based notifications. The KDD'25 paper: Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest placed Conditional Retrieval within a broader multi-embedding retrieval framework for home feed, where explicit interest conditions complement implicit interests extracted from user behavior. In this blog, we describe how CLR evolved from early interest-conditioned retrieval into a broader retrieval system for Pinterest home feed. We focus on three areas: expanding CLR to support more retrieval us