In the two short years Foursquare has been around they have managed to attain almost 7.5 million users that have made over 500 million check-ins at an estimated 10 million venues worldwide. That puts the average number of check-ins at 50 per venue which Foursquare thinks is enough to justify moving forward with real-world recommendations. The technology is far from perfect and will present some interesting engineering challenges for them to overcome. These types of recommendations go far beyond what services like Netflix use in their recommendation process and have a much larger application which has prompted big boys like Facebook and Google to also look into perfecting this technology that bears high risks but also great rewards.
Foursquare recommends you hit the break for details.
They first mentioned an interest in real-world recommendations about 6 months ago and finally released a statement on March 8th about what it is and why it will benefit users.
The idea is pretty simple: tell us what you're looking for and we'll help you find something nearby. The suggestions are based on a little bit of everything - the places you've been, the places your friends have visited, your loyalty to your favorite places, the categories and types of places you gravitate towards, what's popular with other users, the day of the week, places with great tips, the time of day, and so on. We'll even tell you why we think you should visit a certain place (e.g. popular with friends, similar to your favorite spots). You'll find it's helpful for general things like "food", "coffee", "nightlife" (we built in quick access to these searches) and you'll be surprised by what you get when searching for really specific things, like "80s music," "fireplaces," "pancakes," "bratwurst," and "romantic." The more random you get, the more interesting the results get (though be patient with this first release... sometimes we can't find every random thing).
And outside of the "Explore" tab, you'll see some of this thinking starting to surface on the "Me" tab as well. As we started to tinker with our recommendations algorithms, we started to see "expertise" starting to emerge from the data - we're seeing friends that have been to every karaoke place within 10 miles or tried every burger in Los Angeles. The new "Me" tab surfaces some of this, letting you seek guidance from your friends on the categories and places they explore most.
Now, with over half a billion data points, and with every additional check-in and every tip, foursquare gets a little smarter for you, your friends, and the rest of the community. We're already finding this can be just as helpful for finding a brunch spot in your neighborhood as it can be for helping you navigate a new city for the first time.
The main problem to solve if real-world recommendations are going to be successful is the inadequacy of generic machine learning which will take some time, R&D and really creative thinking to overcome but with Foursquare 3.0 you can do your part by simply using their service as normal and continuing to ignore Facebook Places.