Neural Network Case Study: Pinterest

Arjun Chauhan
4 min readMar 1, 2021


The term “artificial intelligence” conjures up images of both the distant future (humanoid robots, self-driving cars) and the hard work it will take to get there. But the technology is already pervasive, and in places you might not realize.

Machine learning and deep learning is more prevalent than we know of. In our shopping cart recommendation system, our google news feed, our youtube homepage, and even in our phone when it closes background applications when it thinks we are not using it.

Nearly all the industries are either being affected by the presence of AI or are directly using it.

Today I would be talking about one such company that is Pinterest and discuss how they are integrating AI or talking technically deep learning in their ecosystem


Pinterest is an American image sharing and social media service designed to enable saving and discovery of information on the internet using images and, on a smaller scale, animated GIFs and videos, in the form of pinboards.

Pinterest is a visual discovery engine for finding ideas like recipes, home and style inspiration, and more.

Where does neural network comes into play?

Internet users appreciate personalization. 80 percent of them are more likely to make a purchase if the experience is personalized. By using AI to analyze mountains of data, Pinterest tailors search results for each of its hundreds of millions of users. This is important because users of pinterest are constantly looking at pins which are nothing but images. So a personalized feed plays a major role here.

Features like surfacing recipe suggestions based on diet or suggesting home decor for a user’s specific taste are available because of deep learning . By understanding the intention behind a simple search, Pinterest’s deep learning models deliver highly personalized results.

If the user has to type complete exact sentences of what they want it would just make them lose interest over time because the users of 21st century lack patience. When a user enters a search term, nearly 75 percent of which are three words or fewer, what is he or she actually looking for? There may not be a clear answer to start, but the deep learning-powered search experience helps draft one. Pinterest apart from being accurate also needs to be easy to use.

Recommendation Systems

One way Pinterest makes recommendations relevant is through a neural network called PinSage, developed in part using the TensorFlow and PyTorch deep-learning frameworks on Amazon Web Services (AWS). The deep-learning model places each image, according to theme, within one giant “graph” of other images.

Three billion images, or “nodes,” form the graph; 18 billion lines connect them. The result is a detailed context for each image, which allows Pinterest to recommend thematically similar images for users, such as charcoal briquettes and grilled meats. Rather than a linear list of results, the user receives a comprehensive guide to what he or she could plan for the weekend.

Pinterest’s deep-learning personalization efforts use more than just users’ search terms to improve. They also learn from what users capture with their phones’ cameras.

Pinterest Lens camera search allows users to search by taking a photo of an object offline and receive results for online recommendations. An apple will return results for related recipes for dishes, such as pie or cider. A photo of a pair of running shoes will result in related shoes, and even athletic clothing to style with it, available for purchase.

When a user takes a photo through Lens, the deep-learning model breaks it into objects, colors, and visual patterns. It then uses that information to make suggestions, which can be either visually similar, thematically similar, or both.

Pinterest is also using Amazon Rekognition, an AI service for image recognition and analysis, as part of its multipronged approach to provide a trustworthy platform for all Pinners. Rekognition automatically filters out content that’s in violation of policies, allowing Pinterest to focus on areas core to its mission of improving discovery.


Pinterest has no trouble attracting new users. And more users means more opportunity (and demand) for personalization and diversification in search.

This growth, coupled with the AI efforts that capitalize on the breadth of data it collects from its users, has meant that Pinterest has tripled its data storage capacity over the last two years.