Using machine learning to predict what file you need next, Part 2

At Dropbox, we are building smart features that use machine intelligence to help reduce people’s busywork. Since introducing content suggestions, which we described in our previous blog post, we have been improving the underlying infrastructure and machine learning algorithms that power content suggestions.

One new challenge we faced during this iteration of content suggestions was the disparate types of content we wanted to support. In Dropbox, we have various kinds of content—files, folders, Google Docs, Microsoft Office documents, and our own Dropbox Paper.

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Using machine learning to predict what file you need next

As we laid out in our blog post introducing DBXi, Dropbox is building features to help users stay focused on what matters. Searching through your content can be tedious, so we built content suggestions to make it easier to find the files you need, when you need them.

We’ve built this feature using modern machine learning (ML) techniques, but the process to get here started with a simple question: how do people find their files? What kinds of behavior patterns are most common? We hypothesized the following two categories would be most prevalent:

  • Recent files: The files you need are often the ones you’ve been using most recently.

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