Machine Learning with Large Networks of People and Places
Foursquare is now aware of over 1.5 billion check-ins from 15 million people at 30 million different places all over the world. Each check-in can be thought of as an edge in a vast network connecting people to each other and to the places that they care about most. Graph-based machine learning algorithms are critical not only for making sense of these networks that emerge out of patterns of human mobility but also for creating useful data-driven products that help people better navigate the real world. In this talk, we will examine two networks that we have observed at foursquare, the Social Graph and the Place Graph, and then discuss various machine learning and big data techniques for better understanding these networks as well as using them to build a novel recommendation engine we call Explore.