Justin Donaldson wins the 1st place award in the Netsci06 poster session with ZMDS: Visualizing Negative Structural Entropy in Queried Network Sub-Graphs.
Justin, a student at Indiana University, who has been collaborating with MyStrands since his internship here last summer, presented this poster (click to enlarge!).
Congratulations, Justin!
Calling all developers! You can now build applications that leverage the power of MycStrands. The OpenStrands 1.0 web services platform is a set of web services for outside developers interested in adding MyStrands functionality to their non-commercial applications.
OpenStrands services allow you to programmatically perform many of the same functions available to you through the MyStrands website using a browser. Use of OpenStrands is free to registered MyStrands users, allowing you to write programs that use MyStrands music recommendation and discovery technology in new and different ways. The platform currently includes account, catalog, recommendation, playlist, tagging, and community services. There is a limit of 5,000 queries per day.
To learn more, register for an OpenStrands subscriber ID and download the OpenStrands Software Development Kit (SDK):
http://www.mystrands.com/openstrands/overview.vm
To help you get started quickly, examples are provided in Java, C#, and Python. You can call OpenStrands services from any language capable of issuing an HTTPS request and parsing the XML response.
The MusicStrands mobile team has released a new version of MyStrands for Windows Mobile 5.0. Previous support for Pocket PC devices has been extended to Smartphone devices.
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The same great MusicStrands technology is now available on cheaper, more portable cellphones. In addition to broadening the platform base, we have added improved support for tags and community.
Not too long ago, several of us were betting how unlikely it was that someone would compose some of the playlists submitted to the site. There is a virtually unlimited number of songs out there, millions and millions of songs and growing everyday. So, with such great diversity and taking into account that we could chose any of the existing songs to include a playlist, we were wondering what the odds are of someone composing a given playlist. We quickly realized that the odds were too small to distinguish from nil if we considered playlists over all of the millions of possible songs, and thinking about how to factor in the relative popularity of songs made our heads hurt.
So we finally agreed to just restrict the problem to deciding the odds that the songs on a particular playlist would occur in a set of random sequences of the songs actually played by our members. Then we started debating whether we should assume a single sequence of songs (what we call a playstream) could only include a single play of each song, or if we could allow playstreams that included repeated plays of songs. Not long after that we decided this problem was really not that interesting, and decided to go to the Cantina instead. Before we left, we asked our Chief Scientist what he thought the odds were, so we could decide who lost the bet and would have to buy the drinks.
He started mumbling to himself about urn problems, multivariate hypergeomtric distributions, reduction to the binomial, and Stirling’s Approximation, and we saw he was going to turn this into a major project, so we decided to leave for the Cantina. When we got back he announced he could give us an approximation to the answer for the first case in which playstreams couldn’t include repeats, but that he still had some “calculator work” to do for the case in which playstreams with could include repeats. We of course had long forgotten the question, but thought we should humor him and pretend to still be interested. He started to explain the mathematics of the problem to us: “Assume we have a population of L songs, and that we select a set of K playstreams from our members of some average length M, what is the odds that at least one playstream includes a particular playlist of N≤M songs. We also assume …”. We decided to just post the analysis for you to read instead. (The short answer for the case without repeated songs: About 10^15-to-1, or a million-billion to one, against someone composing a particular playlist from the songs our members actually play, without considering the relative popularity of songs. No one would have won the bet, so we decided we had done the right thing by going to the Cantina when we did.)
What we hope this illustrates is how we understand and appreciate the uniqueness of each playlist you share with the community. When you compose songs into a playlist, you are capturing a feeling or making a statement that you want to share with the world. We recognize how fortunate we all are that you have chosen to share that feeling or message. We will continue to work to provide you with creative and enjoyable ways to express yourself with and about music; we hope you’ll continue to share your taste in music.
Try our new CBR Playlist Recommendation Engine in our labs!
This project implements a Case-based Reasoning (CBR) approach to musical playlist recommendation. Our CBR approach focuses on recommending new and meaningful playlsits. The Case Base is formed by a large collection of playlists. Then, the CBR system first retrieves from the Case Base the most relevant playlists, then combines them to generate a new playlist, both relevant to the input song and meaningful ordered. This research is developed in a joint project with the Artificial Intellgence Research Institute of the CSIC.
For further information on this project, please take a look at the following paper: Claudio Baccigalupo and Enric Plaza (2006), Case-based Sequential Ordering of Songs for Playlist Recommendation. Proceedings ECCBR-2006, Lecture Notes in Artificial Intelligence, Springer Verlag (to appear).
MusicStrands members can now receive Mashups of multimedia content from their favorite Web 2.0 sites. By selecting any artist, we fetch the artist biography from Wikipedia, photos from Flickr, videos from YouTube, blog posts from Technorati, pesonal goals from 43things, and events from Upcoming. You can also get recommended artists from MusicStrands, and continue the experience by enjoying more multi-media Mashups for those suggested artists.
Enjoy biographies, pictures, videos, blog posts, personal goals, and events related to your favorite artist! and… stay tuned because more are coming!
In the Labs, users of last.fm can get recommended music and users, and now we’ve made this feature even better. In addition to recommending MusicStrands users from a last.fm profile, the feature now displays the artists you have in common, plus other artists you should check out based on your shared tastes:
Strands RSS feed