class_docstr: Defines a common interface for all recommendation models
class_docstr_tok: ["Defines","a","common","interface","for","all","recommendation","models"]
docstr: Trains the model on a sparse matrix of user/item/weight
  
  Parameters
  ----------
  user_items : csr_matrix
      A sparse CSR matrix of shape (number_of_users, number_of_items). The nonzero
      entries in this matrix are the items that are liked by each user.
      The values are how confident you are that the item is liked by the user.
  show_progress : bool, optional
      Whether to show a progress bar during fitting
  callback: Callable, optional
      Callable function on each epoch with such arguments as epoch, elapsed time and progress
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code: def fit(self, user_items, show_progress=True, callback=None):
      """
      Trains the model on a sparse matrix of user/item/weight
  
      Parameters
      ----------
      user_items : csr_matrix
          A sparse CSR matrix of shape (number_of_users, number_of_items). The nonzero
          entries in this matrix are the items that are liked by each user.
          The values are how confident you are that the item is liked by the user.
      show_progress : bool, optional
          Whether to show a progress bar during fitting
      callback: Callable, optional
          Callable function on each epoch with such arguments as epoch, elapsed time and progress
      """
  
file_path: implicit/implicit/recommender_base.py
imports: import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
label_desc: Use image node_id 1 for calling the RecommenderBase obj's underlying member method code with example usage: obj.fit(user_items, show_progress, callback) without return types