lenskit.als.ImplicitMFScorer#

class lenskit.als.ImplicitMFScorer(config=None, **kwargs)#

Bases: lenskit.als._common.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.

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

See the base class ALSBase for documentation on the estimated parameters you can extract from a trained model. See ImplicitMFConfig and ALSConfig for the configuration options for this component.

Changed in version 2025.1: ImplicitMFScorer 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.

Stability:
Caller (see Stability Levels).
Parameters:

  • config (object | None)

  • kwargs (Any)

config: ImplicitMFConfig#

The component configuration object. Component classes that support configuration must redefine this attribute with their specific configuration class type, which can be a Python dataclass or a Pydantic model class.

create_trainer(data, options)#

Create a model trainer to train this model.

new_user_embedding(user_num, user_items)#

Generate an embedding for a user given their current ratings.

Parameters:
Return type:

tuple[lenskit.data.types.NPVector, None]