Spotify: A Product Story Episode 4 - Transcript This is Spotify: A Product Story. And I’m Gustav Söderström. I head up product, engineering, data and design for Spotify. In this podcast, we’ll bring you the biggest product strategy lessons that we’ve learned at Spotify -- from launching the first desktop app all the way to the new audio formats that we’re developing right now. We’ll break down why we made the decisions we made, what worked, what didn’t, and the stories behind the products -- all told in the words of the people who were actually there, who lived it and who made it happen. (00:52) In today’s episode, we’ll explain how we evolved from being an online music library and playlisting service that put all the actual work of curating and organizing the worlds music catalogue in the hands of the user, to a machine-learning powered recommendation engine -- that does the work for you. Over the years, we’ve developed some of the most powerful recommendation algorithms in the world for audio content. All working toward one simple -- but very hard -- goal: delivering listeners the right content at the right time. I’ll be the first to admit that our journey into Machine Learning (or ML, as it’s often called) started with a bit of a misstep. Because for the first few years of Spotify, we didn’t quite see how it would actually fit into our bigger goal of bringing users the perfect listening session. Well, most of us didn’t. Myself included. Oskar Stål: I do remember at one time when we talked about recommendations -- you, Daniel and I -- and we were kind of like, yeah, that's not really core -- can outsource that to someone else. We don't have to consider that kind of our core thing. And then at some point within a few years after that, we changed opinion. That’s Oskar Stål, and to say we changed our opinion is an understatement. So much of an understatement that Oskar’s title is now VP of Personalization at Spotify. And here is how it came about. Early on, in 2008 -- the year that Spotify launched -- a master’s candidate at Stockholm’s Royal Institute of Technology by the name of Erik Bernhardsson came to Spotify to finish his thesis -- And eventually joined the engineering team full-time. But already as a student he 1 Spotify: A Product Story Episode 4 - Transcript saw the potential in giving users personalized recommendations by doing massive matrix multiplications. Oskar Stål: Y eah, I mean, he was like a math genius type of guy. He was doing some math magic to basically do these matrix computations on our data. And back then, the magic was not specifically the algorithm for recommendation. It was more the algorithm for actually doing the matrix multiplication. So it was kind of like a math for approximating in a reasonable way in matrix multiplication. That was what he did. And I think what he wanted to do was just like work on that and make it better. But I think the work he did in the master's thesis, he never quite got the opportunity to improve that for years and years because we always wanted him to work on something else. Gustav Söderström: It's interesting because the theories, they had been around for many years, like, different forms of collaborative filtering or matrix multiplications and stuff. But the tricky thing which not many people had done at the time was actually implement that at scale for I think what was already then tens of millions, if not hundreds of millions of playlists, for example, and later billions. So a lot of the innovation was actually to your point, in first of all figuring out the engineering, because you had to break this up into many computations on different clusters of computers and then approximate some version of the actual algorithm. So there's a lot about actually implementing these things in practice, and that hadn't been done so much at scale. Oskar Stål: Y eah, exactly. This time, you know, it was well known that to do collaborative filtering and it was well known how to do that through matrix multiplication. But the thing people did was much smaller data and much more like reasonable matrixes to work with, so to speak. “Collaborative filtering” is the fancy name for a rather intuitive idea: when a large group of users put the same bunch of tracks next to each other on the same types of playlists over and over again, they’re telling you that those tracks go well together. And that those tracks probably have something in common. Algorithms then use that information to figure out how similar two tracks are mathematically. Based solely on how often they appear on the same playlists. But -- at the time -- running that kind of analysis with a data set as large as ours was incredibly difficult. 2 Spotify: A Product Story Episode 4 - Transcript (05:04) Besides being hard, recommending new music to listeners also seemed secondary. We didn’t discard it, but we didn’t yet see it as core, as our main thing. So -- except for Erik’s work with collaborative filtering, we outsourced the rest of our recommendations to a Massachusetts-based start-up called The Echo Nest. Because as far as discovering new music goes, we thought Spotify was already pretty perfect. All you needed was a really good search bar and an advanced playlisting tool, from there, you could soundtrack your life perfectly! What could be easier than that? It turns out, a lot. A lot of things are easier than that. Which is the basis for our first product strategy lesson. Lesson #1: Build for yourself first. But don’t build for yourself only. It's good to start by building for yourself, because that’s where you have intuition, but pretty quickly you need to ask yourself Mary Meeker’s question from episode 3 - How many of me are there? - or your product will hit a ceiling. We’d built a product that gave just one type of user the perfect listening session. Users just like us. It was the perfect tool for a huge music fan. Someone with an encyclopedic knowledge of bands and genres, who already keeps up with the latest releases and enjoys spending hours at a time combing through the back catalogue and putting together carefully crafted playlists. In other words, someone who could look at a blinking cursor in an empty search bar and -- instead of feeling intimidated and overwhelmed -- would know exactly what they wanted to hear. But guess what? There are only so many die-hard music fans out there. If we wanted to continue to grow, we had to find ways to bring more casual listeners on to the platform. We called it the aficionado problem. Spotify was a powerful product -- it gave you access to almost all the world’s music. But it wasn’t a very helpful product for those who didn’t already have that time or knowledge. In fact, for them it felt like a lot of work. 3 Spotify: A Product Story Episode 4 - Transcript By 2011, we saw the macro wind that we mentioned in episode 3, the shift from curation-focused to recommendation-focused services, that did a lot of the work for you, starting to really pick up speed. And we realized that recommendations needed to become a part of our core strategy and that we needed to hire for it. The only problem was, everyone else also realized it at the exact same time. Here’s Oskar Stål again. Oskar Stål: W hat happened, of course, was that, you know, we didn't really manage to hire anyone because it was impossible to find these machine learning engineers. So I think for a long time we pretty much had like two machine learning engineers in the entire company. And what they did was basically they continued on the same track, like, how do you do collaborative filtering? And I -- the way I remember it was that the first period we continued on basically our own algorithm for Matrix Factorization and used that for quite a while. And basically the work was around iterating on this and making that better. And we were out talking about it. So that was kind of I think the focus for the first year was really just building on that and then applying that to the discovery feed that we were building at the time. We did have one incredibly valuable advantage, something nobody else had -- our library of many hundreds of millions of playlists already back then, which was arguably the largest music curation database in history, growing larger every minute to over 4 billion playlists today!. But that database came with its own set of problems. Oskar Stål: I t was like a nightmare. There were all these strange things that would be popping up all the time. You know, we would be trying to do something about pop and you would have - get Christmas music or you'd get children's music. So it was still really hard for us to make it like, truly work. It was kind of working, but it was not really working. We called these mistakes WTFs -- you definitely d on’t need me to tell you what that stands for -- and they cropped up because it turns out that there is a lot of noise in 100 of millions of playlists, and many users playlist tracks together that aren’t that similar.