There is already one optimisation PR for predict method: lyst/lightfm#105

But sometimes one probably can do it in a simpler way (just using numpy with BLAS):
https://github.com/inpefess/lightfm/blob/predict_comparison/examples/batch_predict/predicting_with_matrix_multiplication.ipynb

@maciejkula what do you think about adding another, say predict_numpy/predict_all me

My questions might seem dumb, but I was trying to understand the project. As seen in the documentation, various parameters go into the model. I was thinking if there is an easy way to do Hyperparameter tuning in spotlight?

Hi, thanks for this nice package!

AlternatingLeastSquares.recommend has a parameter called filter_items, which, apart from the source code, does not have any documentation.

The same parameter is found in RecommenderBase, [`AnnoyAlter

Access to the mcmc hyperparameter is fairly obscure and needs better documentation.

• add documentation to python mcmc module
• add function that generates string parameter description
• add documentation to C fastFM-core/src/ffm.c L. 77 ff

Hyperparameter layout in fm.hyper_param_:

    print (['alpha'] +               
    ['lambda_w'] + ['lambda_V' + str(i) for i in r

• If you discover a bug or regression in either the code or documentation, please report it. We appreciate your time to make the bug report.

  1. Please provide a small and self-contained program which exposes the bug. The small program should have a main() function and use only functions/classes

As a follow-up to #5, it would be nice to have vanity CLI endpoints so users could directly use the code.

Several choices:

• plumber
• Rserve
• opencpu (updated)
• jug (updated)
• something else?