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…

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…

How Pinterest runs Kafka at scale

Pinterest runs one of the largest Kafka deployments in the cloud. We use Apache Kafka extensively as a message bus to transport data and to power real-time streaming services, ultimately helping more than 250 million Pinners around the world discover and do what…

Building a more inclusive way to search

Every day, tens of millions of people search for ideas on Pinterest, whether it’s a recipe to cook for dinner that night or a new hairstyle to try over the weekend. According to a Pinterest study, 70 percent of people use Pinterest to…

How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads

People come to Pinterest in an exploration mindset, often engaging with ads the same way they do with organic Pins. Within ads our mission is to help Pinners go from inspiration to action by introducing them to the compelling products and services that…

How we designed our Continuous Integration System to be more than 50% Faster

Earlier this year, the Engineering Productivity team at Pinterest published a blog called How a one line change decreased our clone times by 99%. In that post, we described how a simple Git configuration sped up clone times in one of our…

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…

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…

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 misinformation, hate speech, and self-harm content with machine learning

Pinterest is an inspiration network where people come for positivity and new ideas, and there’s nothing inspiring about harmful content. As a visual discovery engine with hundreds of billions of Pins…

How we increased engagement using automatic personalized copy testing

Think about the last time you visited a website. Was there a word that helped draw you in? Now think about how that might be different if you spoke a different language, were a different age or grew up in a different part of…

Adventures in big data wonderland: Going down the Pinterest Path

Have you ever started looking for plants on Pinterest, only to end up shopping for pillows instead? Ever find yourself layers of Pins deep into finding a new idea, not sure of how you got there or where you started? When Pinners are in…

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…

Understanding Pins through keyword extraction

Pinterest is well known for its visual discovery engine, but much of our content is also accompanied by text. As a result, it’s important for us to be able to understand text to ensure we recommend relevant ideas to Pinners. In this blog post…

Pin2Interest: A scalable system for content classification

Content understanding is at the core of any great recommendation system. By knowing what each Pin is about, Pinterest can connect Pinners with the most relevant and inspiring content that best matches their interests. Taxonomy of interests The Interest Taxonomy captures popular concepts that appear within Pinterest…

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…

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…

Developing fast & reliable iOS builds at Pinterest (Part one)

At Pinterest we’re focused on helping people discover inspiring ideas, from dinner recipes to try, home and style products to buy, to places to travel. Building the best products for mobile is a critical part of that, with 80% of all Pinners…

Empowering Pinterest data scientists and machine learning engineers with PySpark

Data scientists and machine learning engineers at Pinterest found themselves hitting major challenges with existing tools. Hive and Presto were readily accessible tools for large scale data transformations, but complex logic…

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…

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…

Knit Con through an employee’s eyes

Originally published on Tobi’s personal blog. It’s not every day I get to spend two days with my coworkers making arepas, practicing Tai chi, learning how to plate dishes, and vibing to six different DJs. Two days immersing myself in the deepest caverns of…

Building Pin cohesion

At Pinterest, we’re building a visual discovery engine where ideas become actionable with links to more information. Once a Pin is saved, its linked content may be updated or expire over time, and so it’s critical to know…

Building Pin stats

A few months back, we started building a way for businesses to create and showcase their latest and favorite content on their profiles. We also wanted to make it easier for businesses to understand how their Pins are performing. If a business can…

Introducing inclusive beauty searches for mobile

This week, we’re announcing skin tone ranges is coming to iOS (followed by Android) so Pinners can discover relevant hair and makeup ideas. Over the coming weeks, when Pinners search for beauty ideas on their iPhone or iPad app, a palette representing a range of skin tones will appear. The palette enables them to personalized Pins and videos they see. By combining the latest in machine learning and inclusive product development, we’re able to directly respond to Pinner feedback and build a more useful product. Last year, nearly 60% of the top 100 search terms for skin-related searches involved a tone, such as dark skin, pale skin, and olive skin, which showed us women of all skin types wanted a way to customize their searches. Keep an eye out for a deeper look at the design process behind the product. Acknowledgements: Emma Herold, Lulu Cheng, Long Cheng, Yixue Li, Larkin Brown, Candice Morgan, Xixia Wang, and Laksh Bhasin.