Contextual relevance in ads ranking

Pinterest runs an ads marketplace that balances value for Pinners, partners, and the business. We initially used engagement prediction as our main method to balance the marketplace before eventually adding contextual relevance to increase Pinner value. We have three main surfaces for determining…

Pinterest + ktlint = ❤

Ha pasado casi un año desde que la base de código Android de Pinterest se convirtió en la prioridad de Kotlin, y aproximadamente dos años desde que adoptamos ktlint para el formato y lint de Kotlin. Hoy, compartimos que Pinterest se…

Managing Videos on Android

Video launched on the Pinterest Android app in 2016 with the goal of bringing a seamless video experience to the app. This included the ability to support multiple videos per screen, while dynamically controlling their playback state by automatically pausing them when they’ve scrolled…

La evolución de la búsqueda en Pinterest

This blog article was originally posted in English. To read the English version click here. Cuando Pinterest se lanzó por primera vez, ofrecía una nueva forma de recopilar y organizar ideas de toda la web. Sin embargo, lo que comenzó como un lugar…

Cómo redujimos el tamaño de la aplicación de Pinterest para iOS en más de un 30% / 50 MB

This blog was originally published in English. To read the English version, click here. Todos sabemos que el tamaño de las aplicaciones es importante (el tamaño de descarga[1] y el tamaño de instalación local[2]). Además, existe una correlación…

PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

As we build a visual discovery engine that powers 2B+ Pins, it’s crucial to understand user interests and preferences in…

Real-time User Signal Serving for Feature Engineering

More than 320 million unique visitors come to Pinterest every month to discover inspiring content, which also gives advertisers a unique opportunity to get their products in front of people with commercial intent. Curation…

Building a Label-Based Enforcement Pipeline for Trust & Safety

As Pinterest grows with increasing numbers of users and businesses, providing a safe and trustworthy experience is one of our top priorities. Every day the platform serves billions of Pins and boards to inspire Pinners. With so many Pins and activity, It can…

GokuL: Extending time series data storage to serve beyond one day

At Pinterest, developers rely heavily on Statsboard to monitor their systems and get alerted when issues happen. Last year, we introduced our time series database (aka Goku) which stores and serves the most recent one day’s…

Unifying visual embeddings for visual search at Pinterest

Throughout the years, Pinterest has launched a variety of visual search products powered by computer vision to help people discover new ideas to try and products to buy. We started in 2015 with our visual cropping tool, allowing users to search within…

Calidad y pruebas de las métricas publicitarias en dispositivos móviles

En Pinterest, todos los días ayudamos a nuestros socios de publicidad a llegar a los usuarios a través de diferentes formatos de anuncios y les ofrecemos métricas y análisis sobre el rendimiento de sus anuncios. Mantener la confianza…

Pinterest + ktlint = ❤

It’s been nearly a year since Pinterest’s Android codebase became Kotlin-first, and about two years since we adopted ktlint for Kotlin linting and formatting. Today, we’re sharing that Pinterest has officially taken ownership over the project. Initially, we chose ktlint because of its…

Administrar videos en Android

La función de video se lanzó en la aplicación de Pinterest para Android en 2016 a fin de ofrecer una experiencia de video completamente integrada en la aplicación. Esto incluía la capacidad de admitir varios videos por pantalla…

Detecting image similarity using Spark, LSH and TensorFlow

As a visual platform, the ability to learn from images to understand our content is important. In order to detect near-duplicate images we use the NearDup system, a Spark- and TensorFlow-based pipeline. At the core of the pipeline is a Spark implementation of batch…

The top Pinterest Engineering blog posts of 2018

The most-read posts of the year Three-day no-meeting schedule for engineers A one year PWA retrospective Supporting React Native How Pinterest runs Kafka at scale How switching our domain structure unlocked international growth PinSage: A new graph convolutional neural network for web-scale recommender systems Looking inside the technology that powers Pinterest Building a more inclusive way to search Building a real-time user action counting system for ads Goku: Building a scalable and high performant time series database system We’ll be back in 2019 with more updates from the team. Happy New Year!

Seven best practices for inclusive product design

At Pinterest, our mission is to help you discover and do what you love. We’re building a product for everyone, because the best Pinterest is inclusive and easily usable for people with all levels of abilities. That’s why today we’re sharing our…

Building a Kubernetes platform at Pinterest

Why Kubernetes? Over the years, 300 million Pinners have saved more than 200 billion Pins on Pinterest across more than 4 billion boards. To serve this vast user base and content pool…

Bottoms-Up: How the Pinterest growth team decentralizes team structure

On the growth team at Pinterest, we describe our structure as “bottoms-up”, meaning ideas and responsibilities flow throughout the team in a way that provides a lot of autonomy to work on the projects each person wants to tackle, across a variety of roles. The…

Pinterest Flink Deployment Framework

Background At Pinterest, stream processing allows us to unlock value from real time data for pinners and partners. The Stream Processing Platform team is working on building a reliable and scalable platform to support many critical streaming applications including real-time experiment analytics…

People Behind the Product: Meet Rahim Daya, Head of International Product & Toronto Lead

Rahim Daya, Toronto-native and Pinployee of nearly 4 years, leads Pinterest’s International Product as well as the Tech team in Toronto. Ahead of our virtual Pinterest Engineering: Meet the Toronto Team event, get to know Rahim and learn more about…

Setting Up Snowflake Secondary Roles with Tableau

At Pinterest, we heavily utilize Tableau for our enterprise analytics. In an effort to secure access of sensitive data to the appropriate business units, we moved the enterprise data into a Snowflake data warehouse. Using Snowflake as a data platform, we can create and manage…

Trusting Metrics at Pinterest

Pinterest, like many tech companies, relies heavily on data to help inform decisions and power machine learning. This strategy fundamentally depends on the data being accurate. Over the years we’ve worked to improve the processes behind trusting core decision-making data and ensure metrics remain accurate. How Data Can Go Wrong …

Upgrading Pinterest operational metrics

Recently, Pinterest hit a major milestone: 300 million monthly active users around the world. To reliably serve this user base with our large content corpus, Pinterest’s engineering team maintains thousands of services all working together in tight…

Building board archiving and sorting

Connor Montgomery | Software engineer, Product Engineering A Pinterest tradition we’ve had since our early days is the Make-a-thon, our twist on a hackathon. Make-a-thons bring together employees from different disciplines to build something new. In the past, we’ve had projects range from scaling hacks to AR prototypes to…

Privacy Power-Up: Upgrading Pin Links from HTTP to HTTPS

HTTPS should be everywhere. Your website likely runs over HTTPS, giving your users a secure, encrypted connection, but what about all the outbound links? Can you guarantee that they all use HTTPS? In this post, we describe the steps we took…