How Pinterest Uses Visual AI to Recommend Pins

Visual recommendations flow from images to embeddings to your feed. From what Pinterest sees in billions of Pins to what appears in your personalized home feed.

We recently analyzed Pinterest's recommendation system for 578 million monthly users. This architecture follows patterns we see in modern AI systems because Pinterest operates end-to-end from visual understanding to personalized delivery.

Here's the complete technical walkthrough of how Pinterest uses visual AI to recommend Pins.

Key takeaways

• Pinterest's recommendation system uses deep learning to convert billions of images into visual embeddings that enable similarity matching
• PinSage graph neural network creates connections between visually and thematically related Pins for discovery beyond keyword matching
• Pinnability ranking model scores each Pin for individual users based on predicted engagement from recent activity and similar user patterns
• Kosei acquisition in 2015 brought 400 million product relationships that enhanced personalized product recommendations
• The system adapts to language and region, increasing saves by 10-20% for international users after localization improvements
• Every save, click, and search trains the models to deliver more relevant recommendations over time

Understanding Pinterest's Recommendation Challenge

Before visual AI, Pinterest faced a fundamental problem with recommendation systems at scale.

Text-Based Limitations

Early Pinterest relied on text descriptions and tags to connect users with content. This worked poorly for visual discovery.

A user searching for "cozy living room" might miss relevant Pins tagged differently. Visual style and aesthetic preferences don't translate well into text search.

Scale of Visual Content

Pinterest hosts billions of images. Each image contains multiple visual elements - color palettes, objects, styles, compositions.

Manually tagging or organizing this content was impossible. The platform needed systems that could automatically understand visual content and recommend based on what images actually show.

Individual Preference Complexity

Two users interested in "home decor" want completely different things. One prefers minimalist Scandinavian design, another wants maximalist bohemian style.

The recommendation system needed to understand these subtle visual preferences and serve different content to different users, even when they use the same search terms.

Visual Understanding Through Deep Learning

Pinterest's recommendation system starts by converting images into data the algorithms can process and compare.

Creating Visual Embeddings

Pinterest uses convolutional neural networks to analyze each Pin. These deep learning models process billions of images to create visual embeddings - numerical representations that capture what makes each image unique.

A Pin showing a mid-century modern chair gets encoded with data about its shape, color palette, material texture, and stylistic elements. This encoding happens automatically across the entire Pinterest catalog.

Visual embeddings allow mathematical comparison. Two visually similar Pins have embeddings that are mathematically close to each other, even if their text descriptions differ completely.

Object Detection Within Images

The system doesn't just understand whole images. Computer vision models identify specific objects within each Pin.

A single Pin of a furnished living room gets parsed into discrete elements - the sofa, coffee table, rug, wall art, and lighting fixtures. Each element receives its own visual embedding.

This object-level understanding enables precise recommendations. You can tap any item in a Pin and immediately find visually similar versions of just that item.

The technology enables Pinterest Lens, where you point your camera at something and Pinterest finds similar items, all powered by these visual embeddings comparing camera input to the entire Pin catalog.

PinSage Graph Neural Network

Pinterest built PinSage to solve a specific problem - connecting related Pins through visual and thematic similarity rather than just keyword matching.

Graph-Based Connections

PinSage places each Pin into a massive graph structure. Similar images cluster together based on visual features and thematic connections analyzed from user behavior.

A Pin of a 1950s-style lamp connects more strongly to mid-century modern furniture than to contemporary minimalist designs. The system learns these relationships by analyzing which Pins users tend to save together.

This graph structure means Pinterest doesn't return simple search results. It creates guided pathways through related ideas that feel intuitive and thematically coherent.

Learning from Billions of Interactions

The graph updates continuously. Every time a user saves two Pins on the same board or clicks from one Pin to another, the system notes these connections.

Over time, PinSage learns which visual styles naturally group together based on actual user behavior across millions of people. These learned relationships become the recommendation pathways.

The breakthrough is scale. PinSage handles billions of Pins and trillions of potential connections, updating relationships as new content arrives and user preferences evolve.

This graph-based approach is similar to how Netflix uses machine learning for recommendations, but optimized for visual rather than sequential content.

Pinnability Ranking System

Your home feed doesn't show Pins chronologically or randomly. Pinterest uses Pinnability to score and rank every candidate Pin specifically for you.

Personalized Relevance Scoring

Pinnability is a collection of machine learning models that predict how likely you are to engage with each Pin. Every potential Pin in your feed receives a relevance score.

The models analyze your recent saves, boards you maintain, searches you've performed, and Pins you've clicked. They also consider what users with similar taste patterns engage with.

Pins with higher predicted engagement scores appear first in your feed. This creates a personalized experience that adapts to your evolving interests.

Factors in the Ranking Model

The system considers multiple signals when scoring each Pin for you.

Recent activity weighs heavily. If you've been saving kitchen organization Pins, your feed will prioritize similar content temporarily.

Long-term preferences also matter. The system tracks which topics and visual styles you consistently engage with over weeks and months.

Similar user behavior provides signals too. If users who save the same Pins you do also engage with certain content, that content gets boosted in your feed.

Continuous Adaptation

The ranking happens in real-time. As you interact with Pinterest during a session, your feed adjusts.

Save a few Pins about a new topic, and Pinterest immediately tests showing you more related content. Click on those suggestions, and they appear more frequently. Ignore them, and the system shifts direction.

This continuous learning cycle means your feed stays relevant as your interests change, rather than showing stale recommendations based on old behavior.

Kosei Acquisition for Product Recommendations

Pinterest acquired Kosei in 2015 to strengthen recommendations for shopping and product discovery.

Knowledge Graph of Product Relationships

Kosei specialized in personalized product recommendations. The company brought a knowledge graph containing 400 million relationships between products.

This graph understood connections like "people who like this jacket also like these shoes" at massive scale across product categories.

Pinterest integrated this product relationship knowledge into their existing visual recommendation systems.

Shopping-Focused Features

The Kosei technology powers features like "Make It Yours" that suggest where to buy items similar to Pins you've saved.

If you save a Pin showing a leather jacket, Pinterest uses both visual similarity (from embeddings) and product relationship knowledge (from Kosei) to recommend visually similar jackets you can purchase.

This combination bridges inspiration and commerce. Users discover products through visual browsing, and the recommendation system connects them to purchase opportunities.

The approach mirrors how companies like Nestlé use data analytics to connect product recommendations with actual purchase behavior.

Localization and Language Adaptation

Pinterest's recommendation system doesn't serve the same content globally. It adapts to each user's language and region.

Country-Aware Recommendations

The system factors in your location to ensure culturally relevant content appears in your feed. Users in Paris see more Europe-centric fashion inspiration, while users in Seoul see what's trending in Korea.

This isn't just about translating text. The visual content itself differs - different aesthetic preferences, different product availability, different trending styles.

Measured Impact

Pinterest reported a 10-20% increase in saves by international users after implementing location-aware recommendations.

The improvement came from training models to recognize that users in different regions engage with different visual content, even when searching for the same concepts.

A search for "wedding dress" returns different visual styles in different countries because the models learned regional preferences from user behavior patterns.

Continuous Learning from User Behavior

Every interaction with Pinterest trains the recommendation models to serve better content.

Training Signal from Actions

Each save tells the system "I want more like this." Each click indicates interest. Even time spent viewing a Pin provides feedback.

The system tracks these signals across all users to understand both individual preferences and broader patterns. This data feeds back into the ranking models, graph connections, and visual embeddings.

Balancing Exploration and Exploitation

The recommendation system must balance showing you more of what you already like (exploitation) with introducing new ideas you might enjoy (exploration).

Show only familiar content and users get bored. Show too much unfamiliar content and users don't engage.

Pinterest's algorithms continuously adjust this balance based on your response patterns. If you consistently click on exploratory recommendations, the system shows you more diverse content. If you prefer staying within established interests, it focuses on refinement.

This continuous learning cycle is fundamental to keeping recommendations fresh and relevant over time, similar to patterns we implement in modern data stack architectures for real-time adaptation.

Technical Architecture Summary

Pinterest's visual AI recommendation system operates through multiple connected components working together.

Visual Processing: Convolutional neural networks analyze billions of images to create visual embeddings

Graph Structure: PinSage connects related Pins through learned visual and thematic relationships

Ranking Models: Pinnability scores candidate Pins for each user based on predicted engagement

Product Knowledge: Kosei-derived relationships enhance shopping and product recommendations

Localization: Country and language signals ensure culturally relevant recommendations

Feedback Loop: User interactions continuously train models to improve future recommendations

This architecture processes massive scale - billions of images, hundreds of millions of users, and real-time recommendations that adapt as users interact with the platform.

The system transforms visual content into mathematical representations that enable personalized discovery at scale.

FAQ

How does Pinterest create visual embeddings from images?

Pinterest uses convolutional neural networks to analyze each image and output a numerical vector representing visual characteristics. These embeddings enable mathematical comparison between images for similarity matching.

What makes PinSage different from keyword-based search?

PinSage uses graph neural networks to learn relationships based on visual similarity and user behavior, not text tags. It understands style connections that keywords can't capture.

How does Pinnability determine which Pins to show me?

Pinnability scores each Pin by analyzing your recent activity, long-term preferences, and similar user patterns. Higher-scoring Pins appear first. Scoring updates in real-time as you interact.

What did the Kosei acquisition add to Pinterest's recommendations?

Kosei brought 400 million product relationships that enhanced shopping recommendations. This helps Pinterest suggest where to buy items beyond visual similarity alone.

How do recommendations adapt to different countries?

The models factor in location and language to serve culturally relevant content. This localization increased saves by 10-20% for international users.

Summary

Pinterest's recommendation system uses visual embeddings from deep learning models to understand billions of images. PinSage connects related Pins through graph neural networks. Pinnability ranks content specifically for each user.

The Kosei acquisition added product relationships for shopping. Localization adapts recommendations by region and language. Every interaction trains the models to improve.

This architecture delivers personalized visual recommendations to 578 million monthly users at scale.

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