# understanding-user

Published articles for understanding-user.

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

DevFeed: [Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest](<https://devfeed.tech/articles/pinner-progression-better-use-case-representation-driving-weekly-active-user-growth-at-pinterest-1232.md>)

Original publisher: [Read original article](<https://medium.com/pinterest-engineering/pinner-progression-better-use-case-representation-driving-weekly-active-user-growth-at-pinterest-bd2131ab238a?source=rss----4c5a5f6279b6---4>)

Author: Pinterest Engineering

Published: 2026-07-27T16:01:02Z

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>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [engineering](<https://devfeed.tech/tags/engineering.md>), [growth](<https://devfeed.tech/tags/growth.md>), [interest-exploration](<https://devfeed.tech/tags/interest-exploration.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [retention](<https://devfeed.tech/tags/retention.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [understanding-user](<https://devfeed.tech/tags/understanding-user.md>)

### AI overview

Pinterest introduces Pinner Progression, a recommendation-system program that uses persistent User Interest Clusters to improve discovery and make retention a first-class objective alongside engagement.

### Source excerpt

Part 1 of 2 Authors Personalization (Homefeed): Yuke Yan, Chuxi Wang, Andreanne Lemay, Olafur Gudmundsson, Anna Kiyantseva, Krystal Benitez, Jongho Kim, Jiacong He, Rahul Goutam, James Li, Dylan Wang User Understanding: Simin Li, Sufyan Suliman, Yingjian Ding, Hongbo Deng Data Science: Armando Ordorica, Yan Chen, Ellie Zhang, Karim Wahba Introduction Pinterest's mission is to help people discover the inspiration to create a life they love. Our recommendation system serves hundreds of millions of users, surfacing billions of Pins across interests ranging from home renovation to meal planning to wedding decor. The home feed , where much of that discovery happens, is powered by a multi-stage pipeline spanning retrieval, lightweight scoring, ranking, and re-ranking [1][2][3]. Most of our prior work on this pipeline has been optimized for engagement: clicks, saves, downloads, closeups. These are strong signals of immediate relevance, and optimizing for them has driven significant gains across the system [4][5]. The problem is that engagement and retention are different things. A user can save ten sourdough recipes today and churn next month anyway. All we did was feed them more of what they already liked: we never helped them find something new for next time. This post is one of two that introduces Pinner Progression, a program that reframes the home feed recommendation system around retention as a first-class objective. Our core insight is that by augmenting sequential, action-by-action user understanding with holistic, persistent use-case representation, we can reliably anticipate the user's next moves and start to serve recommendations that ignite their serendipitous discovery. In this post, we introduce the key use-case representation signal: User Interest Clusters (UICs) -- and describe its construction, integration into the recommendation stack, and impact on engagement on retention metrics. A follow-up will cover how we predict unseen UICs and conduct systematic us