I'm currently using it for extending and comparing some matrix factorisation models for recommendation. I am working on the LFM-2b dataset.
For design reasons, part of my interaction matrix (or rather an extended version of it) consists of a block of zeros, which however should NOT be considered while training.
To explain things better, I want to train on a matrix like this:
with A, B, C being sparse matrices and 0 being an empty matrix. The model should NOT use the entries of the bottom-right block for training.
In my understanding, with respect to ALS, this would mean that in the cost function given by
the sum over u, i should leave the elements of the lower-right block out.
If we imagine to be using BPR instead, the negative samples should never belong to this block.
Hi!
First of all, thank you for this great library!
I'm currently using it for extending and comparing some matrix factorisation models for recommendation. I am working on the LFM-2b dataset.
For design reasons, part of my interaction matrix (or rather an extended version of it) consists of a block of zeros, which however should NOT be considered while training.
To explain things better, I want to train on a matrix like this:
with A, B, C being sparse matrices and 0 being an empty matrix. The model should NOT use the entries of the bottom-right block for training.
In my understanding, with respect to ALS, this would mean that in the cost function given by
the sum over u, i should leave the elements of the lower-right block out.
If we imagine to be using BPR instead, the negative samples should never belong to this block.
Is there a way to do this?
Thanks :) Marta