# Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use

DevFeed: [Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use](<https://devfeed.tech/articles/making-user-sequence-data-more-cost-efficient-faster-and-easier-to-use-1231.md>)

Original publisher: [Read original article](<https://medium.com/pinterest-engineering/making-user-sequence-data-more-cost-efficient-faster-and-easier-to-use-2a56a928cae1?source=rss----4c5a5f6279b6---4>)

Author: Pinterest Engineering

Published: 2026-05-21T16:01:00Z

Content type: article

Language: en

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

Topics: [data](<https://devfeed.tech/topics/data.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [production](<https://devfeed.tech/tags/production.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [systems](<https://devfeed.tech/tags/systems.md>), [train](<https://devfeed.tech/tags/train.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

## AI overview

Pinterest describes a redesign of its user-sequence platform for ranking, retrieval, and recommendation workloads. The article explains how enriched event sequences support training datasets, offline analysis, online inference, and latency-sensitive production use cases, with goals of reducing cost, improving extensibility, and simplifying debugging.

## Source excerpt

Authors (listed alphabetically) Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le Zhang Core ML Infra team: Eric Shang, Pihui Wei ML Data team: Connor Votroubek, Yi He User Understanding team: Camilo Munoz, Simin Li If you work on ranking, retrieval, or recommendation systems, you've probably asked for some version of the same thing: "Give me the last N meaningful actions this user took, with the right enrichments, in a format that's easy to train and serve ML models." On paper, that sounds simple. In practice, "user sequences" often become one of the most expensive and fragile parts of the ML data stack. They end up powering everything from training datasets to offline analysis and online inference, so they need to be fresh and complete at the same time. They must remain consistent as you add new events and enrichments. And they have to do all of this while serving latency-sensitive production workloads. This article walks through how we redesigned our user-sequence platform to make these sequences cheaper to run, faster to extend, and easier to debug, while still supporting demanding production use cases. What We Mean by "User Sequence" In this context, a user sequence is an ordered list of recent, relevant events for a user, along with the enrichments (signals) attached to each event. Here, enrichments mean all the extra signals we attach to raw events, so they're useful for models: embeddings (for example, Pin or query representations), contextual features (such as surface, device, or country), and derived attributes or counters that describe how the user interacted with a piece of content over time. A concrete example helps. Imagine a sequence made up of the last 500 engagements a user had with Pinterest Pins. Each event in that sequence might carry a timestamp, an action type, the surface where the action occurred, and a handful of embedding features or categorical attributes. As a data primitive, user sequences are powerful. They capture temporal b