How Shutterstock Uses Machine Learning to Improve the User Experience

How Shutterstock Uses Machine Learning to Improve the User Experience

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.

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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.

The keywording process is especially cumbersome for those working on mobile devices and going through the tedious process of typing on a small screen and keyboard. Contributors know that they need to take keywording seriously and to be as efficient as they can when entering this metadata, yet it’s difficult to maintain focus. Artists tend to be visual or auditory learners and thinkers, and choosing the most effective words can sometimes get in the way of their true craft.

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The mobile team in particular has made great progress recently in helping alleviate some pains for contributors using our mobile app, enabling them to upload in batches, adding a preview screen for their images, autocomplete keywords, and more. But, time and again, we kept coming back to the same issue, keywording, and trying to come up with a better solution for faster and more effective mobile keywording. We knew the best way forward had to do with autotagging and similar approaches that would allow people to click options rather than type in full words repeatedly. Suggestions like these can be helpful, but sometimes they can also get in the way. We all have seen examples of bots that were almost too perfect for their own good. We didn’t want to roll out automated suggestions until we were confident that they would be just as accurate as what our contributors would come up with themselves.

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