music recommender system
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