class_docstr: TriRank: Review-aware Explainable Recommendation by Modeling Aspects.
  
  Parameters
  ----------
  name: string, optional, default: 'TriRank'
      The name of the recommender model.
  
  alpha: float, optional, default: 1
      The weight of smoothness on user-item relation
  
  beta: float, optional, default: 1
      The weight of smoothness on item-aspect relation
  
  gamma: float, optional, default: 1
      The weight of smoothness on user-aspect relation
  
  eta_U: float, optional, default: 1
      The weight of fitting constraint on users
  
  eta_P: float, optional, default: 1
      The weight of fitting constraint on items
  
  eta_A: float, optional, default: 1
      The weight of fitting constraint on aspects
  
  max_iter: int, optional, default: 100
      Maximum number of iterations to stop online training. If set to `max_iter=-1`,         the online training will stop when model parameters are converged.
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model already         pre-trained (R, X, Y, p, a, u are not None).
  
  verbose: boolean, optional, default: False
      When True, running logs are displayed.
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'R':R, 'X':X, 'Y':Y, 'p':p, 'a':a, 'u':u}
  
      R: csr_matrix, shape (n_users, n_items)
          The symmetric normalized of edge weight matrix of user-item relation, optional initialization via init_params
  
      X: csr_matrix, shape (n_users, n_aspects)
          The symmetric normalized of edge weight matrix of user-aspect relation, optional initialization via init_params
  
      Y: csr_matrix, shape (n_items, n_aspects)
          The symmetric normalized of edge weight matrix of item-aspect relation, optional initialization via init_params
  
      p: ndarray, shape (n_items,)
          Initialized item weights, optional initialization via init_params
  
      a: ndarray, shape (n_aspects,)
          Initialized aspect weights, optional initialization via init_params
  
      u: ndarray, shape (n_aspects,)
          Initialized user weights, optional initialization via init_params
  
  seed: int, optional, default: None
      Random seed for parameters initialization.
  
  References
  ----------
  He, Xiangnan, Tao Chen, Min-Yen Kan, and Xiao Chen. 2014.     TriRank: Review-aware Explainable Recommendation by Modeling Aspects.     In the 24th ACM international on conference on information and knowledge management (CIKM'15).     ACM, New York, NY, USA, 1661-1670. DOI: https://doi.org/10.1145/2806416.2806504
code: def _symmetrical_normalization(self, matrix: csr_matrix):
      row = []
      col = []
      data = []
      row_norm = np.sqrt(matrix.sum(axis=1).A1)
      col_norm = np.sqrt(matrix.sum(axis=0).A1)
      for i, j in zip(*matrix.nonzero()):
          row.append(i)
          col.append(j)
          data.append(matrix[i, j] / (row_norm[i] * col_norm[j]))
  
      return csr_matrix((data, (row, col)), shape=matrix.shape)
  
file_path: cornac/cornac/models/trirank/recom_trirank.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from tqdm.auto import tqdm
  from ..recommender import Recommender
  from ...utils import get_rng
  from ...utils.init_utils import uniform
  from ...exception import ScoreException
label_desc: Use image node_id 3 for calling the TriRank obj's underlying member method code with example usage: obj._symmetrical_normalization(matrix) and returns: csr_matrix