the ConversAItion: Season 3 Episode 17

Pinterest Powers Content Personalization with AI

Pinterest is known for its image-centric discovery platform; but with billions of Pins, it takes a very powerful AI engine to create a relevant, personalized experience for each of Pinterest’s 400 million users. Chief Scientist Jure Leskovec joins this week’s episode of The ConversAItion to discuss how the company leverages sophisticated AI to understand and curate content.

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“Data analytics, machine learning and AI are the core of Pinterest...Any user can see any piece of content, any Pin, out of sets of billions of these Pins. So this means we need a very fast, very responsive and very scalable recommender system so that for any user at the given time we will select the right 100 pins out of this set of several billion.”
Jure Leskovec headshot

About Jure Leskovec

Jure has been Chief Scientist at Pinterest since 2015, when the machine learning ads startup he co-founded—Kosei—was acquired by Pinterest. Jure is also a Computer Science Professor at Stanford University, where he specializes in applied machine learning and data science for large interconnected systems. Jure earned his BS in Computer Science from the University of Ljubljana and his PhD in Machine Learning from Carnegie Mellon University. He can be found on LinkedIn here, on Twitter here, and on his website here.

Short on time? Here are 5 quick takeaways:

  1. The journey to Pinterest began with a desire for better ads.

    Jure’s journey to Pinterest began years ago, when he was developing recommender systems as a professor at Stanford University. As his team worked to better understand and predict human behavior, they quickly recognized an opportunity to leverage the technology in advertising. The inspiration, he says, was the all-too-frequent experience of ineffective retargeting—visiting a website once and receiving the same exact ad for days or weeks afterward. At the time, retargeting systems could only serve the same content repeatedly, and if they tried to veer slightly away they completely deteriorated. He co-founded a machine learning start-up called Kosei to deliver a better advertising experience.

    Years later, Jure had a chance encounter with a former student at a Silicon Valley party. The student, employed by Pinterest, was fascinated by Kosei’s work. Things snowballed from there, and ultimately Pinterest acquired Kosei in 2015 and Jure became Chief Scientist. 

  2. AI and machine learning filter through billions of Pins to create a personalized user experience.

    One unique characteristic of Pinterest is that it’s not a follower-based social media—which means every user has access to billions of Pins. To create a relevant, personalized user experience, the platform requires a very powerful recommendation engine. Jure’s team is responsible for developing the highly efficient, responsive and scalable AI-powered recommender system that instantly curates unique newsfeeds for all 400 million Pinterest users. In addition to serving relevant organic content, this technology is also responsible for delivering a relevant in-platform ad experience.

  3. Visual recognition, deep learning and users help Pinterest understand and curate content.

    In order to curate content, the platform needs to first understand what it is. Pinterest is uniquely challenging in this respect because it’s a visual discovery platform, which poses greater difficulty to AI than speech- or text-based material. Pinterest leverages computer vision and sophisticated deep neural networks to identify and categorize the various features of an image to understand what it is and how it relates to other images.

     

    Understanding content then allows Pinterest to identify and serve up similar content—even if the similarities are nuanced. Jure uses fashion and furniture as two examples; the platform can pick up on when a user is interested in a particular style, and serve similar pieces of content even if the color or background is entirely different. 

    Pinterest users themselves are also invaluable in helping the platform to better understand content. If two images are on the same Board, they must have something in common. But also, different users have different ways of classifying the same image. For example, Jure says one user may pin a picture of a fireplace to a board titled “Fireplaces,” while another may classify it under “Vintage Kitchens,” which teaches the platform a bit more about the versatility of this content. Ultimately, this feedback loop not only improves content recommendations but also provides Pinterest with data on that specific user, creating a better content and ad experience for them. 

  4. Machine learning fosters safety for discovery and expression.

    One difference between Pinterest and other social media platforms is that it’s not follower-based. Jure believes that this reality helps people consider it a safe space to explore individual interests rather than publish content for the sake of others. He emphasizes that it’s a priority to Pinterest to keep the platform safe for discovery and expression—meaning the technology does not amplify biases, discriminate or leverage sensitive information to inform better recommendations. The platform’s machine learning is also adept at picking up on malicious users, unsafe pins or spam content and removing it from the site.

  5. In the future, Jure envisions Pinterest guiding users from inspiration through execution.

    Currently, Pinterest is primarily known as a source of inspiration. Users can peruse the site for ideas on home decor, recipes, fashion, travel and more—but for now, the platform stops at ideation. Jure believes the future of Pinterest involves bringing everything onto one platform, where users can not only get inspired, but carry that through to execution. Through a combination of AI, augmented reality and virtual reality, he believes AI can serve as an “assistant” throughout the entire buyer journey. For example, users could browse furniture images on the platform and then see what those items would look like in their own living room. Or they could search through different makeup or clothing options and “try them on” virtually.

Check out more episodes of The ConversAItion.