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implicit-mf's Issues

Very fast ALS on GPUs

Hi Chris, I have been following your work on using MF to recommend music, great work!
We have an extremely fast ALS implementation on GPUs: https://github.com/wei-tan/CuMF/ . It is an order or magnitude faster than Spark ALS.

For now we do not support implicit-mf but it would be straightforward to add that. Maybe you find this interesting to you.
Thanks,
Wei

License?

Christopher what would be the license for your code? Thanks!

Possible bug in CuI derivation?

The per-user/per-item inner loop code does not appear to match the update equations (4) and (5) given in HKV2008.

Specifically, this block: https://github.com/bmcfee/implicit-mf/blob/master/mf.py#L75-L79

CuI = sparse.diags(counts_i, [0])
pu = counts_i.copy()
pu[np.where(pu != 0)] = 1.0
YTCuIY = fixed_vecs.T.dot(CuI).dot(fixed_vecs)
YTCupu = fixed_vecs.T.dot(CuI + eye).dot(sparse.csr_matrix(pu).T)

would match the paper if CuI had the identity subtracted off:

CuI = sparse.diags(counts_i, [0]) - eye

Or am I missing something clever here?

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