Spotify brings you “the right music for every moment,” and thanks to machine learning, they do it well. There are over 180 million active users on Spotify. And, with over 83 million paid subscribers, Spotify’s share of the streaming market is 40% (and growing). How do they do it? On AI at Work, Spotify’s Machine Learning Leader, David Murgatroyd, shared stories and lessons they have learned.David let listeners in on some of the secrets behind Spotify’s Discover Weekly playlist, which reaches 40 million users, sharing how they used the “rule of product” to make the feature even better for their listeners.
“One of our flagship features is called Discover Weekly. Every Monday, we give you a list of 50 tracks that you haven't heard before that we think you're going to like. The ML engine that's the main basis of it, and it's advanced some since, had actually been around at Spotify a bit before Discover Weekly was there, just powering our Discover page. It took someone with product insight to be able to say, well, maybe this broader Discovery page that says because you liked [that], you're going to like this, maybe that's not the right way. Maybe what we need is a playlist because people consume playlists. I think when you're able to pivot the product articulation to fit the capability of the ML, then there's still room to grow there. It's a way to kind of fit it. I think some of the frontiers in recommendation, when it comes to being increasingly contextual, so taking a lot of other features into account that aren't just the long-term identity of the user and the content, but also where is the user right now, what are they doing, what did they just listen to, what did they just do. Those are things that are being worked on.”
David also let us in on the importance of trust within the organization at Spotify and an engineer’s balance between “their customer and their code.”
“It's really important for everyone on the team to trust everyone else on the team, that they're all valuable and that everybody is earning their keep... We're all one team and the front-end person is sitting next to the machine learning engineer, sitting next to the back-end engineer or whatever. In those cases, there needs to be mutual respect. At Spotify and other places, we call it being T-shaped, where you have someone who maybe they do machine learning, but they're also willing to do back end or data or whatever. Not only are they willing, I often say, you want engineers who love their customers more than their code, and they're willing to do whatever they need to do for their customers. They're on this team and what this team needs to do is deliver this thing. It doesn't have anything to do with machine learning. They say, all right, I'm Mr. Machine Learning Engineer. I'm going to jump in on that.”