I like matrix factorization models too ! Most of the netflix prize style models will have problems with this sort of data though. The problem here is that you

I like matrix factorization models too ! Most of the netflix prize style models will have problems with this sort of data though. The problem here is that you only have positive data, and no negative data: you can assume that artists the user listened are positive, but there is no explicit negative votes for artists the user didn’t like. This means that there is no gradient for Funk style (http://sifter.org/~simon/journal/20061211.html http://sifter.org/~simon/journal/20061211.html) SGD solvers to descend. Better matrix factorization models would be something like the weighted ALS (http://labs.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf http://labs.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf) or BPR (http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle_et_al2009-Bayesian_Personalized_Ranking.pdf http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle_et_al2009...) approaches which can handle this sort of thing directly. Having said all that - this post isn’t about CF. Its just trying to explain some basic concepts in information retrieval using finding similar artists as a problem.