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 recalculate_user(self, userid, user_items):
      raise NotImplementedError(
          "recalculate_user is not supported with this model"
      )
  
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 6 for calling the MatrixFactorizationBase obj's underlying member method code with example usage: obj.recalculate_user(userid, user_items) without return types