lenskit.als.ALSConfig
=====================

.. py:class:: lenskit.als.ALSConfig
   :canonical: lenskit.als._common.ALSConfig

   Bases: :py:obj:`lenskit.config.common.EmbeddingSizeMixin`, :py:obj:`pydantic.BaseModel`


   Configuration for ALS scorers.


   .. py:attribute:: embedding_size
      :type:  pydantic.PositiveInt

      The dimension of user and item embeddings (number of latent features to
      learn).


   .. py:attribute:: epochs
      :type:  pydantic.PositiveInt
      :value: 10


      The number of epochs to train.


   .. py:attribute:: regularization
      :type:  pydantic.PositiveFloat | lenskit.data.types.UIPair[pydantic.PositiveFloat]
      :value: 0.1


      L2 regularization strength.


   .. py:attribute:: user_embeddings
      :type:  bool | Literal['prefer']
      :value: True


      Whether to retain user embeddings after training.  If ``True``, they are
      retained, but are ignored if the query has historical items; if ``False``,
      they are not. If set to ``"prefer"``, then the user embeddings from training
      time are used even if the query has a user history.  This makes inference
      faster when histories only consist of the user's items from the training
      set.


   .. py:property:: user_reg
      :type: float



   .. py:property:: item_reg
      :type: float