# Collaborative filter recommends same products to all users

I'm building a collaborative filter using matrix factorization and alternating least squares. For some reason, the math always give me back the same recommendations for all my test users (not always identical, but very similar for each person: only a few different products each time).

The input is not discrete and is of the form:

PersonID, ProductID, numberOfTimesPurchased


I know that collaborative filtering typically uses a rating as input, but I wanted to test with a simple count. I have read this is called an "implied rating".

Do you have any suggestions about changing the input data or adjusting the hyperparameters in order to get a more diverse recommendation pool?