How Shutterstock Uses Machine Learning to Improve the User Experience


Most companies know by now that the key to making smart and strategic decisions is to look at both current and past data as a cornerstone for future business. Business intelligence teams and other analysts are brought on to enable more efficient decision making across every department. This can lead to visible changes for customers or viable improvements to process for employees.

Advances in computer vision have opened up opportunities to apply data like never before. As artificial intelligence has become an increasingly popular topic of late and corresponding neural networks have improved, it’s a great time to revisit how – and when – your company is applying its data.

At Shutterstock, we ask contributors to enter between seven and 50 keywords with each image they submit to our collection. This process helps form the metadata buried beneath the images that serves as a core part of the data we collect, rely on, and use on a daily basis. Data like this guides us in better assessing our needs, at present and in the future. Powered by metadata, we can study both customer behavioral patterns and contributor styles to ensure that there’s structure while the collection grows organically.

Arduous Image Labeling

The keywording process for our 100,000 contributors has long been onerous and time-consuming. But to be a successful stock contributor, you also must be skilled at labeling your images. Effective labeling ensures that images are displayed prominently and discovered by potential customers down the line.

Many stock contributors pay close attention and rely on their own data as they are planning, basing their next shoot on either what’s been popular up until that point or what they project will be an emerging trend or search term during the upcoming season. Keywords, however, remain complex because, for example, there are only so many ways you can describe a tree.