music recommender system i want to build a music recommender system. i have user_id,song,play_counts triplets as my data.I want to do it with collaborative filtering in with i will have USERS row and SONGS columns.It is very similar to movie recommendation but instead of rating,i now have play counts.So, cn i use play count as my matrix values...i will be then using linear regression to predict features of both users and songs simultaneously.
Will it work with play counts to predict music?
Please answer ...i am confused!
Short answer

Yes, you can use the counts as the target you are trying to predict. I would say that you should probably normalize this, so that your target is between 0 and 1. This will help collaborative filtering converge faster.

Long answer

Given the input data you describe, you have to remember the concept of "Garbage in, garbage out"

Now I am not saying that your input is garbage, far from that. What I mean is that if all you know is how many times somebody played a song, this is also all you will be able to learn from and eventually predict.

In your case, it's more of a matter of interpreting the output. Let's imagine that your model works extremely well and is able to predict how many times someone will listen to a song. In that case, it would indeed make sense to recommend them songs that they will indeed listen to a lot because the assumption is that you will listen to songs more often if you like them. But of course, you have to remember that this might not be entirely true