class_docstr: FISM is an item-based model for generating top-N recommendations that learns the
  item-item similarity matrix as the product of two low dimensional latent factor matrices.
  These matrices are learned using a structural equation modeling approach, where in the
  value being estimated is not used for its own estimation.
class_docstr_tok: ["FISM","is","an","item-based","model","for","generating","top-N","recommendations","that","learns","the","item-item","similarity","matrix","as","the","product","of","two","low","dimensional","latent","factor","matrices",".","These","matrices","are","learned","using","a","structural","equation","modeling","approach",",","where","in","the","value","being","estimated","is","not","used","for","its","own","estimation","."]
docstr: get the user history interaction information
  
  Args:
      dataset (DataSet): train dataset
  
  Returns:
      tuple: (history_item_matrix, history_lens, mask_mat)
docstr_tok: ["get","the","user","history","interaction","information","Args",":","dataset","(","DataSet",")",":","train","dataset","Returns",":","tuple",":","(","history_item_matrix",",","history_lens",",","mask_mat",")"]
code: def get_history_info(self, dataset):
      """get the user history interaction information
  
      Args:
          dataset (DataSet): train dataset
  
      Returns:
          tuple: (history_item_matrix, history_lens, mask_mat)
  
      """
      (
          history_item_matrix,
          _,
          history_lens,
      ) = dataset.history_item_matrix()
      history_item_matrix = history_item_matrix.to(self.device)
      history_lens = history_lens.to(self.device)
      arange_tensor = torch.arange(history_item_matrix.shape[1]).to(
          self.device
      )
      mask_mat = (arange_tensor < history_lens.unsqueeze(1)).float()
      return history_item_matrix, history_lens, mask_mat
  
file_path: RecBole/recbole/model/general_recommender/fism.py
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the FISM obj's underlying member method code with example usage: obj.get_history_info(dataset) and returns: history_item_matrix,  history_lens,  mask_mat