Million Song Dataset Challenge - Revision history

Zeno Gantner at 12:09, 5 June 2013

2013-06-05T12:09:53Z

Zeno Gantner at 12:08, 5 June 2013

2013-06-05T12:08:45Z

Zeno Gantner: this still must be wikified a bit

2012-08-15T21:56:35Z

this still must be wikified a bit

Bmcfee: Adding a page for the MSD challenge

2012-07-11T16:40:29Z

Adding a page for the MSD challenge

New page

'''DEADLINE: 11:59 pm, Thursday 9 August 2012 UTC'''

The [http://www.kaggle.com/c/msdchallenge Million Song Dataset Challenge] aims at being the best possible offline evaluation of a music recommendation system. Any type of algorithm can be used: collaborative filtering, content-based methods, web crawling, even human oracles!* By relying on the [http://labrosa.ee.columbia.edu/millionsong/ Million Song Dataset], the data for the competition is completely open: almost everything is known and possibly available.

What is the task in a few words? You have: 1) the full listening history for 1M users, 2) half of the listening history for 110K users (10K validation set, 100K test set), and you must predict the missing half. How much easier can it get?

The most straightforward approach to this task is pure collaborative filtering, but remember that there is a wealth of information available to you through the Million Song Dataset. Go ahead, explore! If you have questions, we recommend that you consult the MSD Mailing List.

Ready to start recommending? Read through our [https://kaggle2.blob.core.windows.net/competitions/kaggle/2799/media/MSDChallengeGettingstarted.pdf Getting Started] tutorial.

For a more technical introduction to the MSD Challenge, see our [http://www.columbia.edu/~tb2332/Papers/admire12.pdf AdMIRe] paper. (Please use this following [http://www.columbia.edu/~tb2332/Papers/admire12.bib citation] when referring to the contest in an academic setting.)

* This contest is for computer models, but if you manage to get recommendations from humans for 110K listeners, we'd like to know how!

The Million Song Dataset Challenge is a joint effort between the [http://cosmal.ucsd.edu/cal/ Computer Audition Lab] at [http://www.ucsd.edu/ UC San Diego] and [http://labrosa.ee.columbia.edu/ LabROSA] at [http://www.columbia.edu/ Columbia University]. The user data for the challenge, like much of the data in the Million Song Dataset, was generously donated by [http://the.echonest.com/ The Echo Nest], with additional data contributed by [http://www.secondhandsongs.com/ SecondHandSongs], [http://musixmatch.com/ musiXmatch], and [http://www.last.fm/ Last.fm]. Follow-up evaluations will be conducted by [http://www.music-ir.org/ IMIRSEL] at the [http://www.lis.illinois.edu/ Graduate School of Library Information Science at UIUC] as part of the Music Information Retrieval Evaluation eXchange ([http://music-ir.org/mirex/wiki/MIREX_HOME MIREX]).

[[Category:Competition]]

← Older revision		Revision as of 12:09, 5 June 2013
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	[[Category: Competition]]		[[Category: Competition]]
		+	[[Category: Music recommendation]]

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 The '''Million Song Dataset Challenge''' aims at being the best possible [[offline evaluation]] of a [[music recommendation]] system.
-Any type of algorithm can be used: [[collaborative filtering]], [[content-based methods]], web crawling, even [[human oracle]]s!* By relying on the [http://labrosa.ee.columbia.edu/millionsong/ Million Song Dataset], the data for the competition is completely open: almost everything is known and possibly available.
+Any type of algorithm can be used: [[collaborative filtering]], [[content-based methods]], web crawling, even [[human oracle]]s!* By relying on the [[Million Song Dataset]], the data for the competition is completely open: almost everything is known and possibly available.
 What is the task in a few words? You have:

@@ Line 1: / Line 1: @@
-'''DEADLINE: 11:59 pm, Thursday 9 August 2012 UTC'''
+The '''Million Song Dataset Challenge''' aims at being the best possible [[offline evaluation]] of a [[music recommendation]] system.
-The [http://www.kaggle.com/c/msdchallenge Million Song Dataset Challenge] aims at being the best possible offline evaluation of a music recommendation system.  Any type of algorithm can be used: collaborative filtering, content-based methods, web crawling, even human oracles!* By relying on the [http://labrosa.ee.columbia.edu/millionsong/ Million Song Dataset], the data for the competition is completely open: almost everything is known and possibly available.
+What is the task in a few words? You have:
-What is the task in a few words? You have: 1) the full listening history for 1M users, 2) half of the listening history for 110K users (10K validation set, 100K test set), and you must predict the missing half. How much easier can it get?
+The most straightforward approach to this task is pure collaborative filtering, but remember that there is a wealth of information available to you through the [[Million Song Dataset]].
 Ready to start recommending?  Read through our [https://kaggle2.blob.core.windows.net/competitions/kaggle/2799/media/MSDChallengeGettingstarted.pdf Getting Started] tutorial.
@@ Line 13: / Line 15: @@
 * This contest is for computer models, but if you manage to get recommendations from humans for 110K listeners, we'd like to know how!
 The Million Song Dataset Challenge is a joint effort between the [http://cosmal.ucsd.edu/cal/ Computer Audition Lab] at [http://www.ucsd.edu/ UC San Diego] and [http://labrosa.ee.columbia.edu/ LabROSA] at [http://www.columbia.edu/ Columbia University]. The user data for the challenge, like much of the data in the Million Song Dataset, was generously donated by [http://the.echonest.com/ The Echo Nest], with additional data contributed by [http://www.secondhandsongs.com/ SecondHandSongs], [http://musixmatch.com/ musiXmatch], and [http://www.last.fm/ Last.fm]. Follow-up evaluations will be conducted by [http://www.music-ir.org/ IMIRSEL] at the [http://www.lis.illinois.edu/ Graduate School of Library Information Science at UIUC] as part of the Music Information Retrieval Evaluation eXchange ([http://music-ir.org/mirex/wiki/MIREX_HOME MIREX]).
-[[Category:Competition]]
+== External links ==