lenskit.algorithms.als.explicit#

Classes

BiasedMF(features, *[, epochs, reg, ...])

Biased matrix factorization trained with alternating least squares [ZWSP08].

class lenskit.algorithms.als.explicit.BiasedMF(features, *, epochs=10, reg=0.1, damping=5, bias=True, rng_spec=None, save_user_features=True)#

Bases: ALSBase

Biased matrix factorization trained with alternating least squares [ZWSP08]. This is a prediction-oriented algorithm suitable for explicit feedback data, using the alternating least squares approach to compute \(P\) and \(Q\) to minimize the regularized squared reconstruction error of the ratings matrix.

See the base class MFPredictor for documentation on the estimated parameters you can extract from a trained model.

Parameters:

features (int) – the number of features to train epochs: the number of
reg (float | tuple[float, float]) – the regularization factor; can also be a tuple
`` (ureg, ireg) – specify separate user and item regularization terms.
damping (float) – damping factor for the underlying bias. bias: the bias model.
True (If) – damping damping.
epochs (int)
reg
bias (Bias | None)
rng_spec (Optional[SeedLike])
save_user_features (bool)

:param fits a Bias with: damping damping. :param rng_spec: Random number generator or state (see seedbank.numpy_rng()). :param progress: a tqdm.tqdm()-compatible progress bar function

property logger#: Overridden in implementation to provide the logger.

prepare_data(data)#

Prepare data for training this model. This takes in the ratings, and is supposed to do two things:

Normalize or transform the rating/interaction data, as needed, for training.
Store any parameters learned from the normalization (e.g. means) in the appropriate member variables.
Return the training data object to use for model training.

Parameters:: data (Dataset)

initial_params(nrows, ncols)#

Compute initial parameter values of the specified shape.

Parameters:

nrows (int)
ncols (int)

Return type:

Tensor

als_half_epoch(epoch, context)#

Run one half of an ALS training epoch.

Parameters:

epoch (int)
context (TrainContext)

new_user_embedding(user, ratings)#

Generate an embedding for a user given their current ratings.

Parameters:: ratings (Series)
Return type:: tuple[Tensor, float | None]

finalize_scores(user, scores, u_offset)#

Perform any final transformation of scores prior to returning them.

Parameters:

scores (Series)
u_offset (float | None)

Return type:

Series