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Jamendo Recommendation SDK
Python
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algorithms
datasets
README
test_dataset.py
test_rmse.py

README

Jamendo Recommendation SDK
--------------------------

The goal of this project is to start a community effort to improve music recommendations on Jamendo.

We want to provide all the tools and test datasets so that everyone can develop, test and submit their own algorithms.


Datasets
--------

We try to have a common format for all the datasets but it's not a strong requirement.

Currently it's : [User ID];[Item ID];[Rating];[Weight];[Date]

 - jamendoalbumreviews : Ratings of the albums by the users. All weights currently equal 0.5 but we could take into account some kind of "karma" for the users.

 - jamendoalbumstarred : Albums added to favourites of users ("stars"). Rating=Weight=1 ; We'll add the Date field on request.
 						This dataset currently doesn't work with the rest of the SDK and needs some love. 
 
 - jamendoalbumallrates : Use a tuple with different rates. This tuple has the form (reviews rate, 1 if starred, 1 if playlisted ). As jamendoartistallrates, 
 						 for such a dataset is used csv.Dataset_ratestuple that makes the eval parsing of the rate tuple and, using the test_algo, compute a
 						 global rate combining the subrates in the tuple. This kind of datasets is yielded using mergedataset.py.
 						 
 - jamendoartistallrates : Here rates are in the form (average of artist's albums reviews from that user, 1 if artist has been starred, #album starred/ #album published)
 						  Use csv.Dataset_ratestuple (see above). This dataset has the advantage to have the greatest data density, but we prefer to consider albums as 
 						  unit to be recoommend!  
 	
You can have some stats on datasets using test_dataset.py. For instance launching 

	python test_dataset.py jamendoalbumallrates.csv 

you get printed the number of users and items the dataset has some rates for, the number of the rates, and finally the matrix % of filing as a density measure. 
To evaluate a recommendation system effectiveness the number of users and items can be served is also important, and the higher density is, the best results can be reached! 
Is useful to compare our data with the one of netflix prize: http://en.wikipedia.org/wiki/Netflix_Prize. As you can see their dataset has a very better density (1.7%) than
our best dataset (jamendoalbumallrates) with density of 0.019! This makes all more challenging for us ;-)
					   
These two should be enough to get some algorithms started but feel free to ask us for more!


Algorithms
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We've included a couple of simple ones to showcase the SDK. You should obviously be able to do better, of to tune the SVD implementation
that seems to yield good results.


Methodology
-----------

Currently we're inspired by the Netflix Challenge, and use RMSE to compare algorithms. You may add others measures of course.

To test an algorithm, use the test_rmse.py command :

	python test_rmse.py [dataset] [algorithm]

You can also use "all" to compare them (defined in test_rmse.py)

	python test_rmse.py jamendoalbumreviews all

Discuss
-------

There's a thread here : http://www.jamendo.com/en/forums/discussion/13986/new-jamendo-recommendation-sdk/

We can also use the github issues feature to improve the SDK
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