Originally Published in 2017 for the Quora Design Blog

Some of the most popular apps today are highly personalized — everyone sees content tailored to them. This is typically powered with supervised machine learning. The basic idea is to keep track of what you (or people like you) see and show you more things that are similar.Facebook's News Feed looks for what you like and click on and shows you similar content. Spotify's Discover weekly is powered by seeing what songs you listen to and see what other songs people are putting on playlists with that song. YouTube's related videos are powered by what other videos people watch with a particular video.

The basic constraint of supervised ML is that the system can only show you more of what it already knows you like. But a good product is also responsible for understanding what you like comprehensively and over time. Machine Learning folks call this the explore/exploit tradeoff because given limited user attention, we can either learn something new about them or show them more about what we know they like. The pain point of this tradeoff is that exploit is more likely to keep the user engaged in the short term.The most common way to do early exploration is what the industry calls “new user experiences” or NUX.


Twitter’s Onboarding

But increasingly as NUXes show up in every product, this feels like burdensome work for users, and the need to collect explore signal with lower friction increases. Also, people's needs and wants evolve so a product needs to be continuously exploring. My goal here is to show you a set of interfaces that collect exploratory signal and ones that exploit signal, and some that do a combination of both

In its simplest terms, an explore interface is one which tries to collect new information to show you relevant content, while an exploit interface is one that uses information it has to try and suggest content for you to consume.Think of these examples as a lens through which to look at your own product to see what you have and what might work for you.


Now, given that abstract survey, let's dig into some examples:



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