Pinterest ML/AI Use Case

Akanksha Chhattri
3 min readFeb 2, 2021


Hello everyone , here i am with a new article which tell us about the ways pinterest uses ML/AI.

Today, machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers. Pretty cool.

Lets understands the terms:


Artificial intelligence (AI) branch of computer science which building smart machines capable of performing tasks that typically require human intelligence.

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs

Machine Learning:

Machine learning is the subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning models rely on patterns and inference instead of manual human instruction.

the process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide

Use Cases of AI/ML

1. Process Automation

2. Sales Optimization

3. Customer Service

4. Security


The home feed is a key way to discover new content, which is valuable to the Pinner, but poses a challenging question. Given the ever increasing number of Pins from various sources, how can we surface the most personalized and relevant Pins? Our answer is Pinnability.

Pinnability is the collective name of the machine learning models we developed to help Pinners find the best content in their home feed.

Powering Pinnability with machine learning

In order to accurately predict how likely a Pinner will interact with a Pin, we applied state-of-the-art machine learning models including Logistic Regression (LR), Support Vector Machines (SVM), Gradient Boosted Decision Trees (GBDT) and Convolutional Neural Networks (CNN). We extracted and tested thousands of textual and visual features that are useful for accurate prediction of the relevance score. Before we launch a model for an online A/B experiment, we thoroughly evaluate its offline performance based on historical data.

the three major components of our Pinnability workflow, namely training instance generation, model generation and home feed serving.

Home feed serving

Home feed is powered by our in-house smart feed infrastructure. When a new Pin is repinned, smart feed worker sends a request to the Pinnability servers for the relevance scores between the repinned Pin and all the people following the repinning Pinner or board. It then inserts the Pins with the scores to the pool that contains all followed Pins. PFY Pins are inserted into the PFY pool with the Pinnability relevance score in a similar fashion.

When a user logs on or refreshes home feed, smart feed content generator materializes the new content from the various pools while respecting the relevance scores within each pool, and the smart feed service renders the Pinner’s home feed that prioritizes the relevance scores.

Given the importance of home feed and the boost in Pinner engagement, Pinnability continues to be a core project in building our discovery engine. they also begun to expand the use of our Pinnability models to help improve our other products outside home feed.

Thank you for Reading …….