With the launch of Apple Music’s “For You” feature, Spotify hand has been forced to unveil it’s own personalization engine in response. Discover Weekly was launched today via a series of well-timed pieces published today across the tech press. The PR push is on to explain to everyone currently evaluating Apple Music on a 3-month trial.
Spotify describes Discover Weekly as, “like having your best friend make you a personalised mixtape every single week.” More specifically, “Updated every Monday morning, Discover Weekly brings you two hours of custom-made music recommendations, tailored specifically to you and delivered as a unique Spotify playlist.”
Spotify, to date, has relied mostly on the social sharing of tracks and manually curated playlists (more than 2 billion!) to enhance the experience of the Spotify subscriber. The coverage today highlights the contribution of Echo Nest, an music intelligence and data platform acquired by Spotify in March of 2014. Reading a number of posts we learn the following:
Inside Spotify’s Hunt for the Perfect Playlist – Wired
Spotify’s internal tool that they use to build playlists has the wonderful moniker, Truffle Pig.
The Echo Nest’s job within Spotify is to endlessly categorize and organize tracks. The team applies a huge number of attributes to every single song: Is it happy or sad? Is it guitar-driven? Are the vocals spoken or sung? Is it mellow, aggressive, or dancy? On and on the list goes. Meanwhile, the software is also scanning blogs and social networks—ten million posts a day, Lucchese says—to see the words people use to talk about music. With all this data combined, The Echo Nest can start to figure out what a “crunk” song sounds like, or what we mean when we talk about “dirty south” music.
maybe some of the songs are bad, or the leadoff song isn’t representative of the rest of the playlist—we’ll try to refine that and give it a shot.” Playlists are made by people, but they live and die by data.
This is another way of underlining the best practices of machine learning. An algorithm is really only as good as it’s training set.
In order to keep a burst of listens from drifting your taste profile towards a fleeting interest, something re/code’s Kafka calls, “the Minions Problem“, Spotify isolates isolated wandering from the core.
Spotify says it solves the Minions Problem by identifying “taste clusters” and looking for outliers. So if you normally listen to 30-year-old indie rock but suddenly have a burst of Christmas music in your listening history, it won’t spend the next few weeks feeding you Frank Sinatra and Bing Crosby. The same goes for kids’ music, which is apparently why Spotify knows I didn’t really like “Happy” that much — it was just in the “Despicable Me 2” soundtrack.
Spotify has built its discovery algorithm on the listening behaviors of its 75 million users while Apple has advertised a more top-heavy approach using designated curators that publish playlists for a mass audience. I have to wonder what happened to all the Genius data that has been gathered after analyzing everyone’s iTunes collections and wonder if we’ll see that being used to balance out Apple’s approach.
I’ve heard that Spotify is working on a “family plan” that would let me break out the collective profile built up on my Spotify account that I share with my kids. That will yield more relevant personal recommendations so I don’t get the hip-hop heavy playlist that greeted me due to my son’s heavy rotation.
I think it’s still very early days and consumers will ultimately benefit in the music recommendation race that has just begun.