lenskit.algorithms.als.implicit#

Classes

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

Implicit matrix factorization trained with alternating least squares [HKV08].
class lenskit.algorithms.als.implicit.ImplicitMF(features, *, epochs=20, reg=0.1, weight=40, use_ratings=False, rng_spec=None, save_user_features=True)#

Bases: ALSBase

Implicit matrix factorization trained with alternating least squares [HKV08]. This algorithm outputs ‘predictions’, but they are not on a meaningful scale. If its input data contains rating values, these will be used as the ‘confidence’ values; otherwise, confidence will be 1 for every rated item.

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

With weight \(w\), this function decomposes the matrix \(\mathbb{1}^* + Rw\), where \(\mathbb{1}^*\) is an \(m \times n\) matrix of all 1s.

Changed in version 2024.1: ImplicitMF no longer supports multiple training methods. It always uses Cholesky decomposition now.

Changed in version 0.14: By default, ImplicitMF ignores a rating column if one is present in the training data. This can be changed through the use_ratings option.

Changed in version 0.13: In versions prior to 0.13, ImplicitMF used the rating column if it was present. In 0.13, we added an option to control whether or not the rating column is used; it initially defaulted to True, but with a warning. In 0.14 it defaults to False.

Parameters:

  • features (int) – The number of features to train

  • epochs (int) – The number of iterations to train

  • reg (float | tuple[float, float]) – The regularization factor

  • weight (float) – The scaling weight for positive samples (\(\alpha\) in [HKV08]).

  • use_ratings (bool) – Whether to use the rating column, if present. Defaults to False; when True, the values from the rating column are used, and multipled by weight; if False, ImplicitMF treats every rated user-item pair as having a rating of 1.

  • rng_spec (Optional[SeedLike]) – Random number generator or state (see numpy_rng()).

  • progress – a tqdm.tqdm()-compatible progress bar function

  • save_user_features (bool)

property logger#

Overridden in implementation to provide the logger.

fit(data, **kwargs)#

Run ALS to train a model.

Parameters:

  • ratings – the ratings data frame.

  • data (Dataset)

Returns:

The algorithm (for chaining).

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)

Return type:

TrainingData

initial_params(nrows, ncols)#

Compute initial parameter values of the specified shape.

Parameters:
Return type:

Tensor

als_half_epoch(epoch, context)#

Run one half of an ALS training epoch.

Parameters:
Return type:

float

new_user_embedding(user, ratings)#

Generate an embedding for a user given their current ratings.

Parameters:

ratings (Series)

Return type:

tuple[Tensor, None]

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