A recommender comic …
Posted by Paul in fun, recommendation on February 23, 2009
Damn you, Mr. Bezos!
Posted by Paul in fun, Music, recommendation on February 22, 2009
Once again, I was blind-sided by the Amazon recommender. I was placing an order for a few books that my wife wanted. Easy enough, and it would only take 5 minutes. But while I was adding Marie’s books to the shopping cart, a recommendation for a new Keith Emerson CD caught my eye. The last thing I bought by KE was not so good, but the reviews for this CD were rather positive – and so I added it to the cart. And then another Keith Emerson Anthology CD was recommened “just for me” – which has some songs I haven’t listened to for years and are still sitting on vinyl in my attic. That 2 CD set found its way into my shopping cart too.
And then, while at the final checkout, the new Ben Fry / Processing book was sitting there, with 13 excellent reviews. How could I pass that up? And so with an extra $80 removed from my wallet, I finally checked out of the store. Really, that should be illegal. But I’m looking forward to the new tunes and the new book.
Last.fm, TechCrunch and the RIAA
Posted by Paul in Uncategorized on February 22, 2009
There was a bit of a kerfuffle on Friday evening when TechCrunch posted a story headlined: Did Last.fm Just Hand Over User Listening Data To the RIAA? – where they “reported” on a rumor that Last.fm had handed over a bunch of data to the RIAA so the RIAA could track down pirates of the new U2 album . The answer to the headline question is an unequivocal “no!”. The folks at Last.fm would never do that, and they have denied it in no uncertain terms.
But still two days later, the headline stands on the front page of TechCrunch, and only readers who venture past the fold will see mention that Last.fm has denied the rumor. Why doesn’t TechCrunch change the headline or post prominently in the first paragraph that Last.fm has denied it the rumor? Why is TechCrunch posting a story based on a single source? No doubt, such headlines bring lots of links and readers to TechCrunch, but it is not responsible journalism. Reporting on rumor, gossip and subsequently failing to correct the false reporting is just bad journalism.
Day #2 at the startup
Posted by Paul in Music, startup, The Echo Nest, web services on February 20, 2009
I just finished day #2 at my new job. Sorry to be so cagey about where I was going, but they wanted to keep it quiet until they could do a press release about it. I see the press release is public now, so I’m free to talk about my new job.
As many of the commenters have guessed, I’ve joined, as the director of the developer community, The Echo Nest – a company that is devoted to providing music intelligence for the next generation of online music application. In this role, I will work with the rest of the Echo Nest team to help grow an active, vibrant music application developer community around The Echo Nest developer API.
I’m really excited to be here at The Echo Nest. The Echo Nest has already established a reputation as a company that provides a new breed of hardcore music intelligence. The Echo Nest goes far beyond the “wisdom of the crowds” model of music discovery (“People who listened to the Beatles also listened to the Rolling Stones”). Instead of just data mining user behavior, The Echo Nest crawls the web to learn everything it can about music by analyzing what the world is saying about music. The Echo Nest also directly analyzes the audio content of music – extracting musical traits such as key, tempo, structure, timbre from the audio.
Home of The Echo Nest
From this analysis of the social context, the user behavior and the actual audio, the Echo Nest gets a deep understanding of the entire world music. It knows which artists are getting the most buzz, which artists are getting stale, how and why artists are related, what words are being used to describe the music. This data goes far beyond the “if you like Britney, you might like Christina” level. The Echo Nest understands enough about music to be able to answer queries such as “make me a playlist of songs with a tempo of 90 beats per minute by an unknown emo artist that sounds something like Dashboard Confessional, and has violins”. The really neat thing is that the Echo Nest is exposing a lot of this functionality in their developer API. This lets anyone who is building a music application to tap into this large resource of music intelligence.
One of my main duties is to be the voice of the developer in the Echo Nest. I’ve written my fair share of music apps, so I have a good idea of some of the many pain points and difficulties that a music application developer has to face, but I’d like to hear more, so if you are developing a music application and you need a particular problem solved let me know – or better yet, post to The Echo Nest developer forums.
I’ll be writing a lot about The Echo Nest in upcoming posts – in particular about using the developer APIs, but I shall still continue to post about all of the interesting things going on the music space – so this blog won’t be too much different from Duke Listens!
An old guy goes to a startup
The new office digs
I’m tired but very happy. Today was my first day at my new gig at a music 2.0 startup. It was a great day … I arrived mid-morning was handed a new laptop (mmmm, that new laptop smell), given a new, completely empty desk and got right down business. As you’d expect in a startup, everyone (except the suits) sits in the same room, the average employee age is under 30 and the staff is entirely male. I’m surrounded by people who eat, drink and breathe music.
The big excitement for the day for the office was the new music setup. One of the guys had just connected a Sonos system to the office stereo which lets the entire office share a single playlist. Anyone in the office can queue any track, by any artist for the whole office to hear. The music for the day was a very eclectic mix – ranging from cool jazz, to power pop – some was a bit hard to work through (the Zappa and the Wedding March) – and there was a surprising amount of 80s pop (the last time I listened to Toto’s Rosanna was in the supermarket).
My first day at work was an interesting contrast to my first day at Sun Microsystems almost 9 years ago. On my first day at Sun, there were about 30 other people who were starting on that same day, and we all spent the first few hours in the Human Resources meeting from hell learning about TPS reports and coversheets. It ultimately took a week to get my login, and email setup by the IT department. And no one played any music at all.
The only downer for the day was the commute – I’ve added an extra 40 minutes to my daily drive – that will take a bit of getting used to.
It is really exciting to be working at a company that I suspect know will be at the heart of the next wave of music on the web. The team is top notch, driven and perhaps most important of all are extreme music fans. The company founders and CEO seem to have a real vision for the future of music. This is the place where things can happen. I feel lucky to be part of the team.
The Father Theresa of the Badge People
There’s a nifty writeup on the SXSW Artist Catalog at Austin 360: A smorgasbord for music festival-goers.
In which I am ridiculed for my music tastes …
Posted by Paul in fun, Music, recommendation on February 16, 2009
I was giving a talk last week about music recommendation at a local college. I was explaining how some of the various online music recommenders work when I noticed that some of the students were chuckling and laughing. I had checked my fly right before I started talking so I knew it wasn’t that. Then some wise guy in the front row made it all clear: “Do you really like to listen to Hilary Duff?”. After a moment of confusion, I realized that I was showing my Pandora radio stations that included the second most infamous Hilary.
My Pandora Radio stations
I tried to explain that I sometimes listen to my Pandora radio with my 13 year-old daughter. I’m not sure that they really believed that.
This evening, my daughter and I were having dinner and talking about music. She’s past the Hilary Duff and Hannah Montana phase. She’s moved onto the Veronicas (check out her latest review) – so we listend to a bit of Veronicas’ radio on Last.fm – which has now been faithfully scrobbled as part of my listening history forever:
I do like listening to music with my daughter. She knows all of the artists, and (seemingly more important), all the back stories, interconnections, failures and gossip about the artists. That seemed to be as important as the music itself. And although it is fun to listen to with my daughter, the music is not really to my taste. I do want to make it clear to anyone, whether it is a class at the local college or a potential future employer that I’m not really that into bubblegum pop.
Making boring homework fun
JF's Pop Artist slide
One of the cool things about working at Sun Labs is all of the very smart interns that come and work for a summer. They are invariably creative, and bring many new ideas to the labs. One intern I worked with a few years back, Jean-Francois, just posted a blog entry about how he wanted to create a slide for a presentation that showed the pop artists from Sweden as a word cloud. Now most of us would have just typed in a few artist names and resized the fonts based on what we though was the approximate popularity of the particular artist. But not Jean Francois. He turned this slide into its own little research project – first trying to scour the popularity data from the Wikipedia, then mining Google search results and finally settling on using the last.fm webservices to get the listener data.
JF took a rather boring assignment – make an oral presentation on Sweden – and made it a learning experience for something that was interesting to him. I am not sure how much he learned about Sweden, but he certainly learned something about web mining, artist name resolution and ambiguity, and using web services. I wonder if Jean Francois’s International Communication’s professor understood the depths of detail that JF went to get the information on that one slide correct.
Anyway, it is a cool post from a JF.
Twisten.fm – Music gone viral.
Posted by Paul in Music, recommendation on February 13, 2009
The fine folks over at Grooveshark have just released Twisten.fm. Like Blip.fm, Twisten.fm combines micro-blogging and music, and like Blip.fm, instead of creating an entirely new microblogging network, Twisten.fm piggybacks on top of the existing Twitter network.
With twisten, you can make tweets with just about any song in the world (Grooveshark has millions of tracks in its catalog) – twisten will automatically generate a twisten tiny url to the song, and if any of your twitter followers click on the link, they are taken directly to the song page on Grooveshark where they can listen to the song.
twisten a song
Here’s what the song tweet looks like on twitter:
Twisten makes it easy for you to tell your twitter followers that you like a song – with Twisten, you just type the name of the song, and twisten does all of the hard work of finding the song in the catalog, generating a tiny URL for the song and posting it to Twitter.
Twisten also makes it easy to listen to music posted by others. If you use the Twisten web app, you can easily listen and browse all of the twisten tweets of your followees or the world at large.
Listening to Twisten tweets
With the twisten app, you just see the Twisten tweets, which makes it a perfect app for browsing through new music. It is easy to listen, since the player is embedded right in the page. You can also listen to the music that is being posted by everyone.
I suspect that Twisten.fm is going to be a really big deal. First and foremost, it is an incredibly viral app. Just by using Twisten, you are telling all the world about it. 18 hours after it’s release, Twisten is #6 on the list of Twitter trending topics.
Second, it doesn’t re-invent the wheel. Instead of building a whole new social network, it sits on top of Twitter, one of the largest existing social networks – it doesn’t have to build up a network from scratch.
Twisten is really neat, I like it a lot – still, there are a few places where it could be improved.
First of all, when listening to music on the Twisten site, the music should never stop when I navigate to a different part of the site. Right now, if I’m listening to a particular tweet, and decide to check out what ‘everybody is listening to’, the music stops. The main Grooveshark app does a much better job of keeping the music playing all the time whilst one navigates through the site.
Currently, when I click on a twisten tiny url in twitter to listen to a song, instead of taking me to Twisten, the URL takes me to Grooveshark. I understand that Grooveshark is hosting all of the music, but it seems to me that if you want to really make Twisten go viral, the links should bring listeners straight to Twisten, where they can listen to the music, and while there, start Twisten their own tweets.
The listening experience on Twisten is a hunt-and-peck style. I see a song, I click on it, I listening to it, and then I go and find the next song. That’s fine when I am exploring for new music, but if I just want to listen to music, I’d like to be able to turn Twisten into a radio station, where I listen to the music that my friends have been twittering. Ideally, I should be able to listen to tweets all day without having to click a mouse button. TheSixtyOne does a great job of keeping the music flowing. Twisten should follow their model.
I wish Twisten.fm would scrobble all my tweets and listens – it’d be great if every music app in the world scrobbled my listening behavior.
Twisten is able to collect all sorts of interesting information about who is listening to what music. I hope they do some interesting things with this data. For instance, they could create a Twitter Music Zeitgest that shows the songs and artists that are rising, popular, or falling. Since Twisten knows what I’ve been listening to, and what I like (because I can ‘favorite’ twisten songs), Twisten should be able to connect me up with other Twisten listeners that have similar tastes so I can use their twitters and listens to guide my own listening. Twisten is going to be able to collect lots and lots of user listener data, so it should be interesting to see what they do with it all.
Twisten has the potential to be the real breakout music application of 2009. It has all the ingredients – a huge catalog of free music, and a viral model that leverages one of the largest and most active social networks. When iLike released it’s facebook app, iLike became the fastest growing music app ever, adding 3 million users in two weeks. Twisten has a good chance to do the same thing.
Music Recommendation and Discovery in the Long Tail
Posted by Paul in Music, music information retrieval, recommendation on February 12, 2009
Over the last couple of years, I’ve been lucky enough to get to know Music Information Retrieval researcher Oscar Celma. Oscar and I collaborated on a tutorial on music information retrieval that we presented at ISMIR 2007. We spent many, many hours on phone, email and IM sifting through every aspect of music recommendation.
This fall, Oscar completed his PhD Thesis. Oscar asked me to be the ‘external reader’ so I spent a good part of my Christmas break reading and re-reading the 230 page thesis. Oscar really has done a phenomenal job at looking at the issues and problems in music recommendation and in particular how they (or more accurately, how they don’t) help you find music in the long tail. Oscar’s analysis of how far different types of recommenders can push you deep into the tail.
Oscar has just published he’s thesis along with some supplementary info and code on the web site: Oscar Celma PhD. If you are involved in Music 2.0, I highly recommend reading it.
Some cool plots:
3D Representation of the long tail
And the abstract …
ABSTRACT
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.
Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations.
In this Thesis we stress the importance of the user’s perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution.
The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user’s relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
Music Machinery 