Pinterest Now Facilitates More Than Five Billion Searches Each Month
A new article on Social Media Today reports that Pinterest has shared some new insights into user search activity as part of an overview of how its search system works, and how it has evolved over time to provide more accurate matches.
As explained by Pinterest:
"The design of Guided Search became used across the industry as a way to click through topics and find the best answer for you. The product paved the way for computer vision-powered visual search. In 2015 we began our work in object recognition within Pins and later brought the technology to camera search to make it possible to search and find recommendations for anything you see online or off.
Search at Pinterest over the years, from Guided Search to search with skin tone ranges and Lens camera to shop. High-resolution image here. Throughout this evolution, search on Pinterest became more visual, and more useful in helping people go from inspiration to action. We brought together the worlds of text and visual search and even created technology to shop the look inside Pins and complete the look. Because people are taking the same image and putting it on different boards, we can learn the deeper semantics of an image. We can then train our systems to emulate the ways Pinners are categorizing images. For example, when you think of a sofa, it isn't a bunch of words -- it's a mix of images and notions of what a sofa is -- and we're teaching the computer this. Every day we're making improvements to relate images to how people think of them, through machine learning. Each time we improve our model we see engagement go up.
We've also launched the ability to shop within Lens and extended our visual search work to augmented reality with AR Try on for lipstick and eyeshadow. Along the way, we made improvements to Related Pins, and it has become one of the most common ways people refine their search -- by clicking on a Pin they like and then scrolling through (in itself a type of visual search).
A big focus for us has also been inclusive product development, to ensure every person feels represented on Pinterest. This has included skin tone ranges so people can filter beauty searches by their skin tone range. We've taken this technology to AR as well, and worked to diminish bias in AI by working with a diverse data set. Ultimately, we want to help Pinners take action on inspiration -- for example, making a purchase in the shop tab or learning a new skill through a creator's Idea Pin. We're continuing investments in building our in-house search framework to surface relevant Pins to the right person at the right time, as well as working to make search results as inclusive as possible, including showing diverse results by default, starting with beauty queries, and more to come!
As explained by Pinterest:
"The design of Guided Search became used across the industry as a way to click through topics and find the best answer for you. The product paved the way for computer vision-powered visual search. In 2015 we began our work in object recognition within Pins and later brought the technology to camera search to make it possible to search and find recommendations for anything you see online or off.
Search at Pinterest over the years, from Guided Search to search with skin tone ranges and Lens camera to shop. High-resolution image here. Throughout this evolution, search on Pinterest became more visual, and more useful in helping people go from inspiration to action. We brought together the worlds of text and visual search and even created technology to shop the look inside Pins and complete the look. Because people are taking the same image and putting it on different boards, we can learn the deeper semantics of an image. We can then train our systems to emulate the ways Pinners are categorizing images. For example, when you think of a sofa, it isn't a bunch of words -- it's a mix of images and notions of what a sofa is -- and we're teaching the computer this. Every day we're making improvements to relate images to how people think of them, through machine learning. Each time we improve our model we see engagement go up.
We've also launched the ability to shop within Lens and extended our visual search work to augmented reality with AR Try on for lipstick and eyeshadow. Along the way, we made improvements to Related Pins, and it has become one of the most common ways people refine their search -- by clicking on a Pin they like and then scrolling through (in itself a type of visual search).
A big focus for us has also been inclusive product development, to ensure every person feels represented on Pinterest. This has included skin tone ranges so people can filter beauty searches by their skin tone range. We've taken this technology to AR as well, and worked to diminish bias in AI by working with a diverse data set. Ultimately, we want to help Pinners take action on inspiration -- for example, making a purchase in the shop tab or learning a new skill through a creator's Idea Pin. We're continuing investments in building our in-house search framework to surface relevant Pins to the right person at the right time, as well as working to make search results as inclusive as possible, including showing diverse results by default, starting with beauty queries, and more to come!
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