“Today, visual search has become one of our most-used features, with hundreds of millions of visual searches every month, and billions of objects detected,” Shahangian says. “Now, we’re introducing three new products on top of our visual discovery infrastructure.”
Pinterest has one of the largest collections of data-rich images on the internet. “We use machine learning to constantly rank and scale 75 billion dynamic objects, from buyable pins to video, and show the right pin to the right person at the best time,” Shahangian says. “Our core focus is helping people discover compelling content, such as products to buy, recipes to make, and projects to try, and machine learning helps us provide a more personalized experience.”
As Pinterest expands its international audience, it’s vital that its service be personalized for people regardless of where they live, what language they speak, or what their interests are, Shahangian says. “Using machine-learned models, we've increased the number of localized pins for countries outside the U.S. by 250 percent over the past year,” he says. “Now each of the more than 150 million people who visit Pinterest monthly see pins most relevant to their country and language.”
In additional, machine learning predicts the relevance of a promoted pin on the site as well as its performance, helping improve the user experience with promoted ideas from businesses.
“We recently added deep learning to our recommendations candidate pipeline to make related pins even more relevant,” Shahangian says. “Pinterest engineers have developed a scalable system that evolves with our product and people’s interests, so we can surface the most relevant recommendations. By applying this new deep learning model, early tests show an increase in engagement with related pins by 5 percent globally.”
Pinterest is constantly developing technologies with the latest in machine learning “to build a visual discovery engine, including making advancements in object detection and scaling an ever-growing corpus of data and the world's data-rich set of images, to people around the world,” Shahangian says.
Building high-dimensional models
Another company using machine learning, software provider Adobe Systems, has worked with supervised and unsupervised machine learning, as well as statistical models to help run its business for years, according to Anandan Padmanabhan, vice president of Adobe Research.
With the transition of Adobe’s business to a cloud-based subscription offering, there were two fundamental drivers that resulted in a need for large-scale machine learning within the company: online channels becoming the primary source for acquiring customers, and the need for driving product engagement and retention at scale across millions of customers. In addition, the data captured on customer engagement with a particular product are far more detailed through machine learning.
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