My latest column for Quanta Magazine makes a connection between high school geometry and recommendation engines used by companies like Netflix.
Adrienne is a Marvel movie fanatic: Her favorite films all involve the Hulk, Thor or Black Panther. Brandon prefers animated features like Inside Out, The Incredibles and anything with Buzz Lightyear. I like both kinds, although I’m probably closer to Adrienne than Brandon. And I might skew a little toward Cora, who loves thrillers like Get Out and The Shining.
Whose movie preferences are closest to yours: Adrienne’s, Brandon’s or Cora’s? And how far are your cinematic tastes from those of the other two? It might seem strange to ask “how far” here. That’s a question about distance, after all. What does distance mean when it comes to which movies you like? How would we measure it?
Using the perpendicular bisector–an elementary and underappreciated idea from high school geometry–we can carve up abstract data spaces into regions that can be fruitfully compared and contrasted. And knowing which region you lie in, and whom you are closest to, can help make predictions about your preferences.
To learn more, read the full article, which is freely available here.