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. In other words, if we imagine to be using either BPR or WARP, the negative samples should never belong to this block.
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.
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. In other words, if we imagine to be using either BPR or WARP, the negative samples should never belong to this block.
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.
Is there a way to do this?
Thanks :)