class_docstr: Caser Model
  
  :Citation:
  
      J. Tang and K. Wang, "Personalized top-n sequential recommendation via convolutional
      sequence embedding", in Proceedings of the Eleventh ACM International Conference on
      Web Search and Data Mining, ACM, 2018.
docstr: Initialization of variables for caser
  
  Args:
      hparams (HParams): A HParams object, hold the entire set of hyperparameters.
      iterator_creator (object): An iterator to load the data.
docstr_tok: ["Initialization","of","variables","for","caser","Args",":","hparams","(","HParams",")",":","A","HParams","object",",","hold","the","entire","set","of","hyperparameters",".","iterator_creator","(","object",")",":","An","iterator","to","load","the","data","."]
code: def __init__(self, hparams, iterator_creator, seed=None):
      """Initialization of variables for caser
  
      Args:
          hparams (HParams): A HParams object, hold the entire set of hyperparameters.
          iterator_creator (object): An iterator to load the data.
      """
      self.hparams = hparams
      self.L = (
          hparams.L
      )  # history sequence that involved in convolution shape
      self.T = hparams.T  # prediction shape
      self.n_v = hparams.n_v  # number of vertical convolution layers
      self.n_h = hparams.n_h  # number of horizonal convolution layers
      self.lengths = [
          i + 1 for i in range(self.L)
      ]  # horizonal convolution filter shape
      super().__init__(hparams, iterator_creator, seed=seed)
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/caser.py
imports: import tensorflow
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
label_desc: Use image node_id 1 to create a new CaserModel object from inherited base classes: SequentialBaseModel with example: obj = CaserModel(hparams, iterator_creator, seed)