class_docstr: Recommends lists of items for users.
class_docstr_tok: ["Recommends","lists","of","items","for","users","."]
docstr: Compute recommendations for a user.
  
  Args:
      user: the user ID
      n(int): the number of recommendations to produce (``None`` for unlimited)
      candidates (array-like):
          The set of valid candidate items; if ``None``, a default set will be used.
          For many algorithms, this is their :py:class:`CandidateSelector`.
      ratings (pandas.Series):
          the user's ratings (indexed by item id); if provided, they may be used to
          override or augment the model's notion of a user's preferences.
  
  Returns:
      pandas.DataFrame:
          a frame with an ``item`` column; if the recommender also produces scores,
          they will be in a ``score`` column.
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code: def recommend(self, user, n=None, candidates=None, ratings=None):
      """
      Compute recommendations for a user.
  
      Args:
          user: the user ID
          n(int): the number of recommendations to produce (``None`` for unlimited)
          candidates (array-like):
              The set of valid candidate items; if ``None``, a default set will be used.
              For many algorithms, this is their :py:class:`CandidateSelector`.
          ratings (pandas.Series):
              the user's ratings (indexed by item id); if provided, they may be used to
              override or augment the model's notion of a user's preferences.
  
      Returns:
          pandas.DataFrame:
              a frame with an ``item`` column; if the recommender also produces scores,
              they will be in a ``score`` column.
      """
      raise NotImplementedError()
  
file_path: lkpy/lenskit/algorithms/__init__.py
imports: from abc import ABCMeta, abstractmethod
  import inspect
label_desc: Use image node_id 1 for calling the Recommender obj's underlying member method code with example usage: obj.recommend(user, n, candidates, ratings) without return types