Josh Constine of Tech Crunch recently wrote, “They call it Truffle Pig, and it’s ProTools for playlists. Punch in parameters like ‘danceability’, date ranges, or emotions and Truffle Pig spits out a set of top jams that would fit your ‘Lovesick 90s Party Starters’ playlist. Truffle Pig is just one of the new musical inventions dreamed up at the sonic skunkworks born from Spotify’s $100 million acquisition of The Echo Nest. Deep inside Spotify’s New York headquarters, the team gave me a peek how the combined company plans to nail recommendations, hook other apps up with legal music, and meld human DJs with algorithms to surface the best songs from the history of recorded sound.” Read more
Posts Tagged ‘Spotify’
Tom Vanderbilt of Wired recently wrote, “The Echo Nest helps music services from Spotify to Rdio and Rhapsody suggest tunes you’ll like. But your playlists also teach its algorithms what movies you’ll watch — and even how you’ll vote… The Echo Nest claims it reaches around 100 million listeners per month, by powering music discovery services such as Spotify, Rdio, Rhapsody and VEVO, and delivering musical connections where none may have existed before… Staring at the sprawling projection up on the wall, which resembles Mark Lombardi’s unsettlingly internecine drawings of political conspiracies, one finds Polish reggae wedged roughly between Romanian pop and K-hop (or Korean hip-hop), closer in musical space to Chicago soul than it is to Finnish hip-hop.” Read more
Spotify is looking for a Software Engineer – Machine Learning in New York, NY. According to the post, “Spotify is looking for exceptional machine learning engineers to help us build the world’s best music recommendations. The ideal candidate has experience in large scale machine learning, collaborative filtering, recommender systems, and/or other related fields. We employ large scale machine learning techniques to mine terabytes of data to come up with great music recommendations, personalize the Spotify experience, classify music, clean our data, and other things. As a ML developer at Spotify you will work on new recommendation algorithms, try out new ways to apply our data, build new product features, and run continuous A/B tests.” Read more
Robert Evatt of Tulsa World reports, “In a small office in downtown Tulsa, Moomat is building a different search that goes beyond inserting text into a search bar. Moomat’s new search technology sifts through databases and automatically finds relationships between points of data, such as suggesting that a person who is looking for President Lincoln might also be interested in learning about his Confederate counterpart Jefferson Davis, or automatically showing how many degrees a specific actor is separated from Kevin Bacon. The company’s goal is to license its technology for use in public and private databases worldwide, but it has now signed on a big-name partner – Spotify, the popular music streaming and discovery service.”
Spotify is looking for a Data Engineer in New York, NY. According to the post, “Spotify is looking for experienced data engineers to join us in NYC on our mission to create the world’s best music player. As a Data Engineer, your job is to work with huge data sets and analyze data using algorithms and machine learning. You will help us improve music recommendations, build top lists, forecast ad delivery, identify trends, and much more. We work on large scale systems processing terabytes of data every day, using a wide range of tools. The data infrastructure is an integral part of Spotify’s backend architecture and it powers most of the user facing features. Some challenging tasks include scaling music recommendations up to millions of users, or how to push out thousands of updates per second to users in real time.” Read more
A new matchmaking app from one of the founders of Adaptive Semantics hit Facebook yesterday. Adaptive Semantics, you may recall, developed the JuLiA semantic text-parsing technology that’s now part of AOL’s toolkit, courtesy of its Huffington Post acquisition.
Kingfish Labs is the startup that created Yoke, and it includes Jeff Revesz as CTO. Rob Fishman, who was Huffington Post’s social media editor, is the CEO of the company, which recently received $500,000 in seed funding. Yoke’s take on the online dating scene is to bring people together with the help of an ontology graph: Its algorithms explore entities, the connections between them, and the strength of those connections to discover common interests between people that just might lead to a real-world bond.
Yoke is deeply connected into the Facebook API, Revesz says. With users’ permission, it accesses basic data such as birthday, location, and education history, and also pulls their Likes in music, bands, artists, movies, books and some general areas outside those categories. Ditto for their closest friends (again, with respect to their privacy settings, so no guarantee as to how far it can get for each individual). Behind the scenes, Yoke mashes up its Facebook Graph data with data from Amazon, Netflix, and Echonest (which powers Spotify radio) to produce an ontology of interest entities for connecting users together. These three sources were chosen, Revesz says, because they’re the easiest to work with, the biggest and the best.
“We’re looking both for similarity information and ontology information,” he explains – that is, for example, how closely two movies might resemble each other, and what entities they might share in common, such as the same director or actors. So, if someone likes one particular movie, the ontology of interest entities can be used to show other people who like similar things.
Get ready for a new wave of Open Graph-enabled applications — and maybe for how they’ll change the game in their industries, too.
At the Facebook F8 developers’ conference today, Mark Zuckerberg discussed how the work the company’s been engaged in over the last year – to make the Open Graph protocol the foundation for mapping all the connections in the world – is going to “make it possible to build a completely new class of applications and rethink a lot of industries at the same time.”
“Hello my name is Eric and I am addicted to music.” Needless to say, I was thrilled when I received one of the early invitations to join Spotify (http://Spotify.com) when it launched recently in the US (if you’re not familiar with Spotify, here’s a good introduction). The service offers a catalog of +15,000,000 tracks, and the audio quality has been consistently excellent.
However, there is one area where I find Spotify severely lacking – discovery. Fortunately, I work in the Semantic Web world, and I recently had the opportunity to play around with Seevl.net, a music discovery service that leverages semantic technology. It’s impressive, and I often find myself using Seevl.net to augment Spotify.
So what is Seevl?