Archive for category recommendation
Here at the Echo Nest just added a new feature to our APIs called Personal Catalogs. This feature lets you make all of the Echo Nest features work in your own world of music. With Personal Catalogs (PCs) you can define application or user specific catalogs (in terms of artists or songs) and then use these catalogs to drive the behavior of other Echo Nest APIs. PCs open the door to all sorts of custom apps built on the Echo Nest platform. Here are some examples:
Create better genius-style playlists – With PCs I can create a catalog that contains all of the songs in my iTunes collection. I can then use this catalog with the Echo Nest Playlist API to generate interesting playlists based upon my own personal collection. I can create a playlist of my favorite, most danceable songs for a party, or I can create a playlist of slow, low energy, jazz songs for late night reading music.
Create hyper-targeted recommendations - With PCs I can make a catalog of artists and then use the artist/similar APIs to generate recommendations within this catalog. For instance, I could create an artist catalog of all the bands that are playing this weekend in Boston and then create Music Hack Day recommender that tells each visitor to Boston what bands they should see in Boston based upon their musical tastes.
Get info on lots of stuff – people often ask questions about their whole music collection. Like, ‘what are all the songs that I have that are at 113 BPM?‘, or ‘what are the softest songs?’ Previously, to answer these sorts of questions, you’d have to query our APIs one song at a time – a rather tedious and potentially lengthy operation (if you had, say, 10K tracks). With PCs, you can make a single catalog for all of your tracks and then make bulk queries against this catalog. Once you’ve created the catalog, it is very quick to read back all the tempos in your collection.
Represent your music taste – since a Personal Catalog can contain info such as playcounts, skips, and ratings for all of the artists and songs in your collection, it can serve as an excellent proxy to your music taste. Current and soon to be released APIs will use personal catalogs as a representation of your taste to give you personalized results. Playlisting, artist similarity, music recommendations all personalized based on you listening history.
These examples just scratch the surface. We hope to see lots of novel applications of Personal Catalogs. Check out the APIs, and start writing some code.
Here’s a ‘sponsored link’ purchased by Amazon on the popular social news site Reddit. The text of the ad is a excerpt from Roger Ebert’s scathing review of the movie Caligula (the review opens with “Caligula is sickening, utterly worthless, shameful trash” and it goes downhill from there).
I found it a bit curious to see Amazon using such a horrendous review in an ad, but those folks at Amazon are clever. The ad has over 300 comments by Reddit readers meaning that many thousands have probably clicked on the ad to see which movie Ebert was talking about. Hundreds of comments, thousands of visitors all from a 10 word excerpt of a scathing review of the movie. Not too shabby.
Update – the commenters point out that the sponsored link is not purchased by Amazon but by Reddit user qgyh2 who makes money via Amazon’s affiliate program. As Dan says – “he picks headlines that are likely to encourage people to click on the link and then he makes money from whatever they buy while they are at Amazon.” So, qgyh2 is the clever one (but Amazon gets cleverage points for encouraging this kind of stuff via their affiliate program).
Update 2 – flx points out that qgyh2 actually works for Reddit. Here’s more info – ‘He’s helping us experiment with new ways of supporting the site. We weren’t really ready to announce this one yet, or even decide if it’s going to be a permanent fixture. When we do, there will be a blog post about it.’
There’s an interesting piece in the New Yorker about the future of listening. The article focuses on Pandora and MOG and the challenges of making the online listening experience. Author Sasha Frere-Jones concludes with this:
While using these services, I kept thinking about an early-eighties drum machine called the Roland TR-808, which has seduced generations of musicians with its heavy kick-drum sound and the oddly human swing of its clock. Whoever programmed this box had more impact on dance music than the hundreds of better-known musicians who used the device. Similarly, the anonymous programmers who write the algorithms that control the series of songs in these streaming services may end up having a huge effect on the way that people think of musical narrative—what follows what, and who sounds best with whom. Sometimes we will be the d.j.s, and sometimes the machines will be, and we may be surprised by which we prefer.
Read the article:
The tradition of the old-style Radio DJ continues on Internet Radio sites like Radio Paradise. RP founder/DJ Bill Goldsmith says of Radio Paradise: “Our specialty is taking a diverse assortment of songs and making them flow together in a way that makes sense harmonically, rhythmically, and lyrically — an art that, to us, is the very essence of radio.” Anyone who has listened to Radio Paradise will come to appreciate the immense value that a professionally curated playlist brings to the listening experience.
I wish I could put Bill Goldsmith in my iPod and have him craft personalized playlists for me - playlists that make sense harmonically, rhythmically and lyrically, and customized to my music taste, mood and context . That, of course, will never happen. Instead I’m going to rely on computer algorithms to generate my playlists. But how good are computer generated playlists? Can a computer really generate playlists as good as Bill Goldsmith, with his decades of knowledge about good music and his understanding of how to fit songs together?
To help answer this question, I’ve created a Playlist Survey – that will collect information about the quality of playlists generated by a human expert, a computer algorithm and a random number generator. The survey presents a set of playlists and the subject rates each playlist in terms of its quality and also tries to guess whether the playlist was created by a human expert, a computer algorithm or was generated at random.
Bill Goldsmith and Radio Paradise have graciously contributed 18 months of historical playlist data from Radio Paradise to serve as the expert playlist data. That’s nearly 50,000 playlists and a quarter million song plays spread over nearly 7,000 different tracks.
The Playlist Survey also servers as a Radio DJ Turing test. Can a computer algorithm (or a random number generator for that matter) create playlists that people will think are created by a living and breathing music expert? What will it mean, for instance, if we learn that people really can’t tell the difference between expert playlists and shuffle play?
Ben Fields and I will offer the results of this Playlist when we present Finding a path through the Jukebox – The Playlist Tutorial – at ISMIR 2010 in Utrecth in August. I’ll also follow up with detailed posts about the results here in this blog after the conference. I invite all of my readers to spend 10 to 15 minutes to take The Playlist Survey. Your efforts will help researchers better understand what makes a good playlist.
Save the date: 26th September 2010 for The Workshop on Music Recommendation and Discovery being held in conjunction with ACM RecSys in Barcelona, Spain. At this workshop, community members from the Recommender System, Music Information Retrieval, User Modeling, Music Cognition, and Music Psychology can meet, exchange ideas and collaborate.
Topics of interest
Topics of interest for Womrad 2010 include:
- Music recommendation algorithms
- Theoretical aspects of music recommender systems
- User modeling in music recommender systems
- Similarity Measures, and how to combine them
- Novel paradigms of music recommender systems
- Social tagging in music recommendation and discovery
- Social networks in music recommender systems
- Novelty, familiarity and serendipity in music recommendation and discovery
- Exploration and discovery in large music collections
- Evaluation of music recommender systems
- Evaluation of different sources of data/APIs for music recommendation and exploration
- Context-aware, mobile, and geolocation in music recommendation and discovery
- Case studies of music recommender system implementations
- User studies
- Innovative music recommendation applications
- Interfaces for music recommendation and discovery systems
- Scalability issues and solutions
- Semantic Web, Linking Open Data and Open Web Services for music recommendation and discovery
I hope Amazon aggregates their Whispersync data and give us some Last.fm-style charts about how people are reading. Some charts I’d like to see:
- Most Abandoned - the books and/or authors that are most frequently left unfinished. What book is the most abandoned book of all time? (My money is on ‘A Brief History of Time’) A related metric – for any particular book where is it most frequently abandoned? (I’ve heard of dozens of people who never got past ‘The Council of Elrond’ chapter in LOTR).
- Pageturner – the top books ordered by average number of words read per reading session. Does the average Harry Potter fan read more of the book in one sitting than the average Twilight fan?
- Burning the midnight oil – books that keep people up late at night.
- Read Speed – which books/authors/genres have the lowest word-per-minute average reading rate? Do readers of Glenn Beck read faster or slower than readers of Jon Stewart?
- Most Re-read – which books are read over and over again? A related metric – which are the most re-read passages? Is it when Frodo claims the ring, or when Bella almost gets hit by a car?
- Mystery cheats – which books have their last chapter read before other chapters.
- Valuable reference – which books are not read in order, but are visited very frequently? (I’ve not read my Python in a nutshell book from cover to cover, but I visit it almost every day).
- Biggest Slogs – the books that take the longest to read.
- Back to the start – Books that are most frequently re-read immediately after they are finished.
- Page shufflers – books that most often send their readers to the glossary, dictionary, map or the elaborate family tree. (xkcd offers some insights)
- Trophy Books – books that are most frequently purchased, but never actually read.
- Dishonest rater - books that most frequently rated highly by readers who never actually finished reading the book
- Most efficient language – the average time to read books by language. Do native Italians read ‘Il nome della rosa‘ faster than native English speakers can read ‘The name of the rose‘?
- Most attempts – which books are restarted most frequently? (It took me 4 attempts to get through Cryptonomicon, but when I did I really enjoyed it).
- A turn for the worse – which books are most frequently abandoned in the last third of the book? These are the books that go bad.
- Never at night – books that are read less in the dark than others.
- Entertainment value – the books with the lowest overall cost per hour of reading (including all re-reads)
Whispersync is to books as the audioscrobbler is to music. It is an implicit way to track what you are really paying attention to. The data from Whispersync will give us new insights into how people really read books. A chart that shows that the most abandoned author is James Patterson may steer readers away from Patterson and toward books by better authors. I’d rather not turn to the New York Times Best Seller list to decide what to read. I want to see the Amazon Most Frequently Finished book list instead.
I’m excited! Next week I travel to Austin for a week long computer+music geek-fest at SXSW. A big part of SXSW is the music – there are nearly 2,000 different artists playing at SXSW this year. But that presents a problem – there are so many bands going to SXSW (many I’ve never heard of) that I find it very hard to figure out which bands I should go and see. I need a tool to help me find sift through all of the artists – a tool that will help me decide which artists I should add to my schedule and which ones I should skip. I’m not the only one who was daunted by the large artist list. Taylor McKnight, founder of SCHED*, was thinking the same thing. He wanted to give his users a better way to plan their time at SXSW. And so over a couple of weekends Taylor built (with a little backend support from us) The Unofficial Artist Discovery Guide to SXSW.
The Unofficial Artist Discovery Guide to SXSW is a tool that allows you to explore the many artists attending this year’s SXSW. It lets you search for artists, browse popularity, music style, ‘buzzworthiness’, or similarity to your favorite artists – and it will make recommendations for you based on your music taste (using your Last.fm, Sched* or Hype Machine accounts) . The Artist Guide supplies enough context (bios, images, music, tag clouds, links) to help you decide if you might like an artist.
Here’s the guide:
Here’s a quick tour of some of the things you can do with the guide. First off, you can Search for artists by name, genre/tag or location. This helps you find music when you know what you are looking for.
However, you may not always be sure what you are looking for – that’s where you use Discover. This gives you recommendations based on the music you already like. Type in the name of a few artists (even artists that are not playing at SXSW) or your SCHED*, Hype Machine or Last.fm user name, and ‘Discover’ will give you a set of recommendations for SXSW artists based on your music taste. For example, I’ve been listening to Charlotte Gainsbourg lately so I can use the artist guide to help me find SXSW artists that I might like:
If I see an artist that looks interesting I can drill down and get more info about the artist:
I use Last.fm quite a bit, so I can enter my Last.fm name and get SXSW recommendations based upon my Last.fm top artists. The artist guide tries to mix things up a little bit so if I don’t like the recommendations I see, I can just ask again and I can get a different set. Here are some recommendations based on my recent listening at Last.fm:
If you’ve been using the wonderful SCHED* to keep track of your SXSW calendar you can use the guide to get recommendations based on artists that you’ve already added to your SXSW calendar.
In addition to search and discovery, the guide gives you a number of different ways to browse the SXSW Artist space. You can browse by ‘buzzworthy’ artists – these are artists that are getting the most buzz on the web:
Or the most well-known artists:
You can browse by the style of music via a tag cloud:
And by venue:
Building the guide was pretty straightforward. Taylor used the Echo Nest APIs to get the detailed artist data such as familiarity, popularity, artist bios, links, images, tags and audio. The only data that was not available at the Echo Nest was the venue and schedule info which was provided by Arkadiy (one of Taylor’s colleagues). Even though SXSW artists can be extremely long tail (some don’t even have Myspace pages), the Echo Nest was able to provide really good coverage for these sets (There was coverage for over 95% of the artists). Still there are a few gaps and I suspect there may be a few errors in the data (my favorite wrong image is for the band Abe Vigoda). If you are in a band that is going to SXSW and you see that we have some of your info wrong, send me an email (email@example.com) and I’ll make it right.
We are excited to see the this Artist Discovery guide built on top of the Echo Nest. It’s a great showcase for the Echo Nest developer platform and working with Taylor was great. He’s one of these hyper-creative, energetic types – smart, gets things done and full of new ideas. Taylor may be adding a few more features to the guide before SXSW, so stay tuned and we’ll keep you posted on new developments.
When I test-drive a new music recommender I usually start by getting recommendations based upon ‘The Beatles’ (If you like the Beatles, you make like XX). Most recommenders give results that include artists like John Lennon, Paul McCartney, George Harrison, The Who, The Rolling Stones, Queen, Pink Floyd, Bob Dylan, Wings, The Kinks and Beach Boys. These recommendations are reasonable, but they probably won’t help you find any new music. The problem is that these recommenders rely on the wisdom of the crowds and so an extremely popular artist like The Beatles tends to get paired up with other popular artists – the results being that the recommender doesn’t tell you anything that you don’t already know. If you are trying to use a recommender to discover music that sounds like The Beatles, these recommenders won’t really help you – Queen may be an OK recommendation, but chances are good that you already know about them (and The Rolling Stones and Bob Dylan, etc.) so you are not finding any new music.
At The Echo Nest we don’t base our artist recommendations solely on the wisdom of crowds, instead we draw upon a number of different sources (including a broad and deep crawl of the music web). This helps us avoid the popularity biases that lead to ineffectual recommendations. For example, looking at some of the Echo Nest recommendations based upon the Beatles we find some artists that you may not see with a wisdom of the crowds recommender – artists that actually sound like the Beatles – not just artists that happened to be popular at the same time as the Beatles. Echo Nest recommendations include artists such as The Beau Brummels , The Dukes of Stratosphear, Flamin’ Groovies and an artist named Emitt Rhodes. I had never ever seen Emitt Rhodes occur in any recommendation based on the Beatles, so I was a bit skeptical, but I took a listen and this is what I found:
Update: Don Tillman points to this Beatle-esque track:
Emitt could be the sixth Beatles. I think it’s a pretty cool recommendation
Last week NPR’s On the Media had a special show called ‘The Future of Music’ – all about the current state of the music industry and where it is all going. The hour is broken into a number of sections:
- Facing the (Free) music – about what has happened in the 10 years since Napster – Yep Spotify gets a mention. Choice quote by Hilary Rosen – “Napster was a missed opportunity’
- They Say That I stole this – about the legalities of sampling (with interviews with Girl Talk among others)
- Played Out – interview with John Scher about the state of live music
- Teens on Tunes – interviews with teens about where they get their music. Answer: Limewire
- Charting the Charts – interesting piece about the charts – the history of billboard, and the next generation of tracking including an interview with Bandmetrics founder Duncan Freeman (way to go Duncan!)
- Why I’m not afraid to take your money – interesting interview with Amanda Palmer about how artists make money in today’s music world
One thing that they didn’t talk about at all was music discovery – no mention of the role of the critic, music blogs, hype machine, no discussion of the role social sites like last.fm play in music discovery, no mention of automated tools for music discovery like recommenders and playlisters. Maybe next year, when everyone has access to infinite music, we’ll see more emphasis on discovery tools.
It was a great show. Highly recommended: NPR’s On the Media Special Edition: The Future of the Music Industry
This year ISMIR concludes with the 1st Workshop on the Future of MIR. The workshop is organized by students who are indeed the future of MIR.
MIR, where we are, where we are going
Session Chair: Amélie Anglade Program Chair of f(MIR)
Meaningful Music Retrieval
Frans Wiering – [pdf]
- Some unfortunate tendencies: anatomical view of music – a dead body that we do autopsies, time is the loser Traditional production-oriented/
- Measure of similarity: relevance, surprise
- Few interesting applications for end-users
- bad fit to present-day musicological themes
- We are in the world of ‘pure applied research’ – no truth interdisciplinary between music domain knowledge and computer science.
- Music is meaningful (and the underlying personal motivation of most MIR researchers).
- Meaning in musicology – traditionally a taboo suject
- Subjectivity: an indivds. disposition to engage in social and cultural interactions
- Meaning generation process – we have a long-term memory for music -
- Can musical meaning provide the ‘big story line’ for MIR?
The Discipline Formerly Known As MIR
Perfecto Herrera, Joan Serrà, Cyril Laurier, Enric Guaus, Emilia Gómez and Xavier Serra
Intro: Our exploration is not a science-fiction essay. We do not try to imagine how music will be conceptualized, experienced and mediated by our yet-to-come research, technological achievements and music gizmos. Alternatively, we reflect on how the discipline should evolve to become consolidated as such, in order it may get an effective future instead of becoming, after a promising start, just a “would-be” discipline.Our vision addresses different aspects: the discipline’s object of study, the employed methodologies, social and cultural impacts (which are out of this long abstract because of space restrictions), and we finish with some (maybe) disturbing issues that could be taken as partial and biased guidelines for future research.
Notes: One motivation for advancing MIR – more banquets!
- MIR is no more about retrieval than computer science is about computers
- Music Information Retrieval – it’s too narrow
- Music Information or Information about Music?
- Interested in the interaction with music information
- We should be asking more profound questions
- content tresasures in short musical exceprts, tracks performances etc.
- music understanding systems
- Most metadata will be generated in the creation / production phase (hmm.. don’t agree necessarily, all the good metadata (tags, who likes what) is based on context and use which is post-hoc)
- Instead of automatic analysis – build systems to help humans help humans
- Music like water? or Music as dog!!! – a friend – companion -
- Personalization, Findability
- Music turing test
Good, provocative talk
Oral Session 2: Potential future MIR applications
Session Chair: Jason Hockman (McGill University), Program Chair of f(MIR)
Machine Listening to Percussion: Current Approaches and Future Directions – [pdf]
Abstract: approaches have been taken to detect and classify percussive events within music signals for a variety of purposes with differing and converging aims. In this paper an overview of those technologies is presented and a discussion of the issues still to overcome and future possibilities in the field are presented. Finally a system capable of monitoring a student drummer is envisaged which draws together current approaches and future work in the field.
- Challengs: Onset detection of isolated drum strokes
- Onset detection and classification of overlapping drum sounds
- Onset detection and classification in the presence of other instruments
- Variability in Percussive sounds . Dozens of criteria effect the sounds produced (strike velocity, angle, position etc.)
- Future Research Areas
- Extension of recognition to include the wide variety of strokes. (open hh, half-open hh, hh foot splash etc)
MIR When All Recordings Are Gone: Recommending Live Music in Real-Time - [pdf]
Marco Lüthy and Jean-Julien Aucouturier
Recommending live and short lived events. Bandsintown, Songkick, gigulate … pay attention to this paper.
- Recommendation for live music in real-time
- Coldplay -> free album when you get a ticket to a coldplay concert – give away the music
- NIN -> USB keys in the toilet – which had strange recording on the file – strange sounds – an FFT of the sounds showed phone number and GPS coordinates – turned into a treasure hunt to a NIN nails concert.
- Komuso Tokugawa – an avatar for a musiciaon in second life. Plays in second life, twitters concert announcements (playing wake for Les Paul in 3 minutes)
- ‘How do we get there in time?’
- JJ walked through how to implement a recommender system in second life
- Implicit preference inferred from how long your avatar listens to a concert (Nicole Yankelovich at Sun Labs should look at this stuff)
- Great talk by JJ – full of energy – neat ideas. Good work.
- Global Access to Ethnic Music: The Next Big Challenge?
Olmo Cornelis, Dirk Moelants and Marc Leman
- The Future of Music IR: How Do You Know When a Problem Is Solved?
Eric Nichols and Donald Byrd