How ads indexing works at Pinterest

Introduction Ads indexing is a data processing pipeline that builds servable ad documents for the ads delivering stack at Pinterest, including ads targeting, retrieval, ranking and auction. The pipeline converts ad campaigns in the raw format to the servable format via a heavy computational…

Geo-blocking media content on Pinterest

Pinterest engineers are tasked with showing Pinners the best ideas related to their interests, which includes taking into account signals like location. As part of these efforts, we built a video and image platform to enable a…

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…

En Pinterest desarrollamos compilaciones para iOS rápidas y confiables

En Pinterest nos enfocamos en ayudar a las personas a descubrir ideas inspiradoras, desde recetas para una cena, hasta productos de estilo y para el hogar que comprar y lugares a los que viajar. Para ello, es…

Keeping Related Pins fresh

If you’ve ever heard someone mention “the Pinterest Rabbit Hole,” they’re likely referring to Related Pins, the ideas you see below a Pin you’ve tapped. Related Pins is a feed of content relevant to the closeup Pin and accounts for 40 percent of engagement…

How Pinterest Fights Spam Using Machine Learning

Hundreds of millions of people regularly visit Pinterest to visually discover inspiring ideas among billions of Pins. Inspiration is a high bar and we must be vigilant in ensuring that Pinners don’t see spam, harmful content or misinformation. To enforce our…

Improving data processing efficiency using partial deserialization of Thrift

At Pinterest we’ve worked to greatly improve data processing efficiency. One quote that resonates with our unique approach is from writer Antoine de Saint-Exupéry: “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.” Ultimately…

Goku: Building a scalable and high performant time series database system

Co-authored by Jinghan Xu, Jian Wang & Tian-Ying Chang, Engineers At Pinterest, developers rely on Statsboard to monitor their systems and discover issues. A reliable and efficient monitoring system is very important for development velocity. Historically, we’ve used OpenTSDB to ingest and…

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 …

Using PID controllers to diversify content types on home feed

Every day millions of Pinners visit the home feed to find inspiration on Pinterest. As a member of the home feed ranking team, it’s my job to not only figure out what relevant pins to show Pinners but also to make sure that…

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 = ❤

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…

Multi-task Learning and Calibration for Utility-based Home Feed Ranking

Home feed is one of the most important surfaces at Pinterest that drives a significant portion of engagement from the more than 400+ million people who visit each month. From a…

Interest Taxonomy: A knowledge graph management system for content understanding at Pinterest

Interest Taxonomy at Pinterest We recently began rolling out the beta version of Pinterest Trends, a new tool that gives a view of the top U.S. search terms on Pinterest within the past 12 months, and a look into when search terms peak for…

PinalyticsDB: A Time Series Database on top of Hbase

PinalyticsDB is Pinterest’s proprietary time series database. At Pinterest, we rely on PinalyticsDB as a backend for storing and visualizing thousands of time series reports such as the sample case below, segmented by country. PinalyticsDB was built several years ago on…

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…

Pre-Submit Integration Tests For Ads-Serving

Introduction & Background The ads-serving platform is the highest-scale, highest-complexity, and highest-velocity recommendation service at Pinterest. Our ads business is growing and expanding, and the ads engineering team is iterating quickly to continue to improve the system. Therefore, it is vital to keep the system healthy, in order…

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…

Looking inside the technology that powers Pinterest

Pinterest began as a small startup, and has grown to a company that serves +175B Pins to +250M people. While we have one of the largest datasets online, we have 600+ engineers, and so technology needs to be accessible for each person to play their part in…

What it’s like to interview at Pinterest

Interviews have traditionally been thought of as an unpleasant but necessary experience. Candidates often find them stressful and many come in having had poor interview experiences in the past. However, interviews are the clearest opportunity for companies to articulate and demonstrate…

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…

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…

Four lessons in making Pinterest faster on Android

As engineers, we intuitively know that a faster app is a better experience for Pinners, but we also have proof that when web pages load faster, user growth improves. Last year, we doubled down on performance across our platforms so…

Building a dynamic and responsive Pinterest

In 2015, the majority of content on Pinterest was pregenerated for users prior to login. It was stored statically in HBase and served directly upon entering the service. (More details can be found in the blog post Building a smarter home feed.) …

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…