class_docstr: MatrixFactorizationBase contains common functionality for recommendation models.
  
  Attributes
  ----------
  item_factors : ndarray
      Array of latent factors for each item in the training set
  user_factors : ndarray
      Array of latent factors for each user in the training set
  num_threads : int
      The number of threads to use for batch recommendation calls and fitting the
      model. Setting to 0 will use all CPU cores on the machine
class_docstr_tok: ["MatrixFactorizationBase","contains","common","functionality","for","recommendation","models",".","Attributes","--","--","--","--","--","item_factors",":","ndarray","Array","of","latent","factors","for","each","item","in","the","training","set","user_factors",":","ndarray","Array","of","latent","factors","for","each","user","in","the","training","set","num_threads",":","int","The","number","of","threads","to","use","for","batch","recommendation","calls","and","fitting","the","model",".","Setting","to","0","will","use","all","CPU","cores","on","the","machine"]
code: def user_norms(self):
      if self._user_norms is None:
          self._user_norms = np.linalg.norm(self.user_factors, axis=-1)
          # don't divide by zero in similar_items, replace with small value
          self._user_norms[self._user_norms == 0] = 1e-10
      return self._user_norms
  
imports: import warnings
  import numpy
  from scipy.sparse import csr_matrix, lil_matrix
  from ..recommender_base import ModelFitError, RecommenderBase
  from .topk import topk
label_desc: Use image node_id 11 for calling the MatrixFactorizationBase obj's underlying member method code with example usage: obj.user_norms() and returns: self