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

This is from the documentation at http://docs.seldon.io/api-oauth.html#actions

The item attribute definition is:
string name [attr_id 1]
string artist [attr_id 2]
enum category [attr_id 3]
double price [attr_id 4]
Where:
category is the enumeration
** (pop [value_id 1], rock [value_id 2], rap [value_id 3])
a range definition is created for the price ** (<10 [value_id 1], 10-20 [value_id

Came up in jfkirk/tensorrec#31

It would be nice to have an arg to re-order the batches every epoch while fitting.

• Add shuffle_batches arg to fit() and fit_partial() that shuffles the batch order every epoch if True
• Add tests for the arg

In your code, you just check whether the randomly generated negative sampling exists in the training set, therefore, some negative samples may exist in the test set, Is it reasonable to do so?

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

https://stackoverflow.com/questions/50257614/tensorflow-eager-and-tensorboard-graphs
This link seems to point out the problem.

Right now, I disable tensorflow eager execution by tf.compat.v1.disable_eager_execution().