1000 years of music to listen to

Youngmoo Kim had asked everyone on the ISMIR recommendation panel to briefly summarize what they think will happen in the next 5 years of music recommendation. However, it's really hard to do so in less than 4 minutes.

From my perspective the most interesting development in the next 5 years will be the increase in the amount of data we will be working with. We will have a lot more of the same and we will have additional sources. Combining different sources is an interesting challenge, but the main challenge will be to scale things up.

All of this additional information will lead to much better recommendations overall, and in particular in the long tail. We'll be able to detect new trends such as an up-and-coming artists or the emergence of a new subgenre much sooner. We'll be able to localize recommendations a lot more.

At the same time there'll obviously be a lot more music to choose from. I'd roughly estimate about 200 million tracks in Last.fm's recommendation engine in the next 5 years. That's more than 1000 years of continuous listening. Subcultures and genres will emerge faster.

In 5 years recommendation engines will have a much better understanding of listeners. While Last.fm, Pandora, and others already do a lot to understand what listeners are interested in, I'm sure there is room for a lot more improvements.

Another interesting development I'm looking forward to is data portability and openness. In particular, I'm looking forward to users being able to move freely with their personal data from one site to another. Similar to how Last.fm users can already today allow other sites to access their data.

I'm also expecting to see a lot more artists and labels embrace recommendation engines. Similar to SEO (search engine optimization) more artist and labels will try to do a lot more REO (recommendation engine optimization).

Obviously mobile applications will be very important, and so will mobile music recommendations. And I have no doubts that human-to-human recommendations (which are strongly supported by Last.fm) will continue to be very important, maybe even more than they are today.

Anthony Volodkin made a great point that we'll see a lot happen in terms of user interfaces, how recommendations are represented, how recommendations are explained. I believe Paul Lamere would call that steerable and transparent recommendations. I like how Last.fm explains recommendations by explaining a recommendation in terms of a bunch of similar artists I'm familiar with. However, there's obviously room for a lot more. On the other side, I wouldn't mind no explanation at all, as long as every recommendation is spot on. Anthony also made a great point by pointing to playful discovery systems.

I believe it was Brian Whitman who said that recommendations will be a commodity. Every music site will have recommendations. Just like almost every web 2.0 site out there supports tagging. I believe Etienne Handman made a similar point when he previously explained to me why he expects the word "personalization" to fade away. Everything will be personalized, it will be the default option.

Creepy Recommendations

I just got some recommendations from an algorithm that were so good that it was creepy. (One of the recommendations was this video.)

It made me realize how such recommendations can be a nice shortcut for a machine into someones heart. (Although it only takes a few wrong recommendations to be kicked out again.)

I wonder if in the near future I'll have gotten used to the idea that an algorithm attached to my attention profile data will know me better than any human being could (and I'm not just talking about music).

The Echotron

Having read Paul's post yesterday, I instantly applied for a beta invite. In fact, while writing this blog post I'm listening to a very pleasant stream of music from the echotron. I'm impressed! It couldn't be much easier to get started. It's very easy to quickly add a bunch of artists I like to my profile, and I even get to listen to any search results. The recommendations are pretty good, definitely a lot better than many others I've seen out there... and they are sometimes rather different from the ones I get at Last.fm (in a good way). However, despite being biased, I'd still argue that Last.fm's recommendations are better ;-)

Anyway, it seems the echotron could easily turn into more than just a site built to showcase the echo nest's APIs.

ISMIR Highlight: Recommendation Tutorial

Paul Lamere and Oscar Celma did a wonderful job presenting the recommendation tutorial. I wouldn't be surprised if this turns out to be my personal highlight of ISMIR 2007. They presented an overview of all the standard techniques used for recommendations, they talked about the typical (and unsolved) problems recommenders face, they had plenty of examples, and they also presented results from an evaluation of recommenders. The parts I personally liked best were the in depth analysis of tags and folksonomies, the part they called "novelty and relevance" (with interesting ideas on how to reach deeper into the long-tail), the analysis of artist similarity networks, and the evaluation of recommenders. They also made an interesting point about how nice it would be to have something like a Netflix competition for music recommendation. I'm guessing the slides of the tutorial will be online soon. I highly recommend having a look at them ;-)

I only attended the recommendation tutorial, but I've been told the other tutorials were also really well done. Seems like this year's ISMIR is not only the best in terms of number of papers submitted, number of people attending, best location ever, but also best content ever! ;-)

Btw, Paul's blogging about ISMIR in case you haven't noticed yet. And a number of pictures have already been uploaded to flickr tagged ismir2007.

Listening

The best thing about working in MIR research is that it’s part of the job to spend lots of time listening to music. Which makes me realize that I've been working very hard this weekend ;-)

I spent the last hours listening to music listening I found playing around with a recommender. It's one of those recommenders which takes one of my favorite tracks as input and returns a list of similar tracks. Seeing how amazing some of the recommendations are makes me wonder if I’ll ever again bother to browse lists of similar artists to find new music.

Good Recommendations (2)

Inspired by Paul’s ongoing evaluation I tried my own tiny little evaluation.

As seed I used Le Volume Courbe. I recently stumbled upon Charlotte while browsing the Last.fm music profiles of friends.


I wanted to find more of the same (unfortunately she only recorded one album), and the most obvious place to start was the Last.fm similar artist list. There’s lots of good music there, but nothing that I enjoyed as much. I also browsed the top listener profiles, and profiles of people who commented on the Last.fm page for Le Volume Courbe. Again I found lots of great music, but not really more of the same.

Next obvious stop was Pandora, but they never heard of Le Volume Courbe before. So I tried iLike but they didn’t know about any similar artists and ZuKool couldn’t help either. MyStrands had a long list, but after sampling the first two on the list I had the impression that they are pointing me in the wrong direction (too much towards electronic music). Amazon had some interesting recommendations (first time I heard about shoegaze) but not really more of the same. And finally my flat mate recommended some great and related music, but also not really more of the same.

So my preliminary verdict is: either there isn't more of the same out there, or the music recommendation services I tried need to be improved.

UPDATE: I just had a look at the AMG similar artist list. There's some interesting recommendations there, some of which I had already stumbled upon while browsing similar artists on Last.fm, but still nothing that's truly more of the same.

UPDATE Part 2: I just tried the AMG Tapestry Demo suggested by Zac in the comments. It's a lot more convenient than browsing the AMG pages, and it's similar to Last.fm's similar artist radio stations (except that it's only 30 second previews). Nevertheless, there were some recommendations on the list that I appreciated (and hadn't found in the AMG list of similar artists). However, somehow the recommendations seem to be missing some of the "darkness" I like about Le Volume Courbe. Anyway, it's great to have so many nice ways of exploring similar artists.

UPDATE Part 3: I just sampled some of the artists on the list Paul posted in the comments. Whatever system he's using, it's doing a great job in surfacing very unknown artists, some of which are even hardly present on Last.fm, and most of which seem to be present on myspace (which makes me wonder if his recommendation machine is gathering information from there?). Again, I failed to find more of the same. However, a number of the recommendations were related (in particular, some were related with respect to the lo-fi, singer-songwriter, DIY aspects I like about Le Volume Courbe), and since none of the recommendations in his list had shown up in any of the previous recommendations I had seen (at least as far as I can remember) it was rather refreshing to hear them. It's really nice to see a recommendation machine that has such a strong emphasis on surfacing rather unknown artist.

UPDATE Part 4: Oscar suggested in the comments to try the hype machine, which I did. I found some interesting comments about Le Volume Courbe there, but didn't really find more of the same. I also tried the much hyped SeeqPod, but they not only failed to find music related to Le Volume Courbe, but also gave some not so trust worthy recommendations for Mozart. Others I tried and that didn't have any results were musicmatch and musicplasma.

UPDATE Part 5: Ian mentioned that ZuKool now has Le Volume Courbe in their catalog. I gave it a quick try by adding all the songs from the album into the list (because I didn't like how when I'd only choose one track all other tracks from the same album would show up in the recommendation list). The results were as refreshing as those from Paul's list (none of them I had previously seen in a recommendation list) and they were interesting to listen to. However, I couldn't find more of the same. Btw, getting Celeste Zepponi's "Jesus Is Here" recommended when searching for similar music to Le Volume Courbe suggests that ZuKool completely ignores socio-cultural information, which makes it an interesting alternative to all other music recommenders I use.

Good Recommendations

Paul launched a very interesting survey on music recommendations. The results will be presented at their tutorial in 2 weeks at ISMIR and I'm sure presentation slides will be available online after that. I highly recommend participating :-)

Trying to answer the questions I realized how difficult it can be to recommend music given just one artist. Would it be good to recommend someone a rather unknown (= not so popular) artist when they are looking for something similar to an extremely popular artist (like The Beatles)? Or would it be better to recommend similar artists which are also very popular? Btw, in the case of The Beatles, would it really make sense to recommend John Lennon and other members of the group? And if someone is looking for music similar to a not so well known artist, would it make sense to recommend similar but popular artists? Or is it safe to assume that this person already knows these?

Evaluating recommendations is another very interesting topic… and I’m very curious what the outcomes of Paul’s survey will be.

Exciting times for music recommendations

The Filter recently secured another round of financing worth USD 5 million. Not too long ago MyStrands secured a massive USD 25 million. And today's job announcement on the music-ir mailing list sounds like BMAT has started to build their own social music recommendation web site, too. Btw, wouldn't it make sense for BMAT and MyStrands to work together? Anyway, that's just a tiny sample of all the companies working on music recommendations, some of the startups seem very promising.

There’s still a very long way to go. But with every step forward, music listeners will find it easier to discover new artists. And artists will find it easier to find an audience. (Btw, if you use Last.fm you might notice some larger steps forward in the next months.)

It's amazing how much has happened since 2001 (when I finished my Master thesis on a related topic). It’s fun to be working in such a dynamic environment. And it's never been easier to discover amazing music.