Talk on Foursquare Location and Taste Data at Cambridge

I was very busy early this month traveling to the UK and putting together a talk for the first Workshop on Urban Data Science (WUDS15), which took place at Cambridge University on December 14th.

Because I had limited time, I briefly covered 2 issues. But both are fascinating data/technological insights that we are developing at Foursquare. I think they both also showcase the types of contextual data that more and more apps and websites are going to be using over the next few years.

The first is venue search, which Foursquare has been at essentially since it was founded in 2009. The idea is that we want to take information from your phone and translate that into an actual, named venue, like “Starbucks in the Danbury Mall”. There are certainly some simple heuristics (tricks) to get at this – but we’ve continually improved upon it and the hardware in your pockets have been improving by leaps and bounds as well. Check out the Washington Square Park picture!

The second is insights from our taste data, which has been a specialty of mine for several years now. If you want to give people good recommendations, there are a lot of things that can go wrong. Time of day, time of year, and country/city are all very important when it comes to recommending things to do. Obviously – we have a strong personalized component as well, but you really need to nail these wider properties in order to get the system looking smart.

So – here’s the video!

As always, I'm open to feedback on both the content of my talk and the presentation style. I did do some rehearsal, but I feel I could have made it a little more high energy. Still - I got some audience engagement. I was told there were a lot of Italians in the audience, and they reacted well to the graph where Italy eats dinner later!

I will post below my slides and the official title and abstract of the talk:

Slides
Cambridge Urban Data Science Talk

Title
Places and Tastes: Understanding Where People are and What They Do There

Abstract
Foursquare has built a large data pipeline to match geographical coordinates to specific venues and shapes. This talk will be about the evolution of this system and how we found features to give us maximum accuracy. It was also cover the reasons why the technology was developed and how it powers our products and our business. Foursquare also has a taste model which is used to understand which keywords are associated with venues, cities, and times. Tastes have given us a richer understanding of these venues and we will look at some of the fascinating data that compares tastes across venues, cities, languages, times of week, seasons.