In this chapter, we have two contributors, Kyle Teague from GetGlue, and someone you are a bit more familiar with by now: Cathy O’Neil. Before Cathy dives into her talk about the main topics for this chapter—times series, financial modeling, and fancypants regression—we’ll hear from Kyle Teague from GetGlue about how they think about building a recommendation system. (We’ll also hear more on this topic in Chapter 7.) We then lay some of the groundwork for thinking about timestamped data, which will segue into Cathy’s talk.
We got to hear from Kyle Teague, a VP of data science and engineering at GetGlue. Kyle’s background is in electrical engineering. He considers the time he spent doing signal processing in research labs as super valuable, and he’s been programming since he was a kid. He develops in Python.
GetGlue is a New York-based startup whose primary goal is to address the problem of content discovery within the movie and TV space. The usual model for finding out what’s on TV is the 1950’s TV Guide schedule; that’s still how many of us find things to watch. Given that there are thousands of channels, it’s getting increasingly difficult to find out what’s good on TV.
GetGlue wants to change this model, by giving people personalized TV recommendations and personalized guides. Specifically, users “check in” to TV shows, which means they can tell other people they’re watching a show, thereby creating a timestamped ...