lenskit.als.ImplicitMFScorer
============================

.. py:class:: lenskit.als.ImplicitMFScorer(config = None, **kwargs)
   :canonical: lenskit.als._implicit.ImplicitMFScorer

   Bases: :py:obj:`lenskit.als._common.ALSBase`


   Implicit matrix factorization trained with alternating least squares
   :cite:p:`hu:implicit-mf`.  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 :math:`w`, this function decomposes the matrix
   :math:`\mathbb{1}^* + Rw`, where :math:`\mathbb{1}^*` is an :math:`m
   \times n` matrix of all 1s.

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

   .. versionchanged:: 2025.1
       ``ImplicitMFScorer`` no longer supports multiple training methods. It always uses
       Cholesky decomposition now.

   .. versionchanged:: 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.

   .. versionchanged:: 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


   .. py:attribute:: config
      :type:  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.


   .. py:method:: create_trainer(data, options)

      Create a model trainer to train this model.



   .. py:method:: new_user_embedding(user_num, user_items)

      Generate an embedding for a user given their current ratings.