class_docstr: SHAN exploit the Hierarchical Attention Network to get the long-short term preference
  first get the long term purpose and then fuse the long-term with recent items to get long-short term purpose
class_docstr_tok: ["SHAN","exploit","the","Hierarchical","Attention","Network","to","get","the","long-short","term","preference","first","get","the","long","term","purpose","and","then","fuse","the","long-term","with","recent","items","to","get","long-short","term","purpose"]
code: def calculate_loss(self, interaction):
      inverse_seq_item = interaction[self.INVERSE_ITEM_SEQ]
      user = interaction[self.USER_ID]
      user_embedding = self.user_embedding(user)
      seq_output = self.forward(inverse_seq_item, user)
      pos_items = interaction[self.POS_ITEM_ID]
      pos_items_emb = self.item_embedding(pos_items)
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          neg_items_emb = self.item_embedding(neg_items)
          pos_score = torch.sum(seq_output * pos_items_emb, dim=-1)
          neg_score = torch.sum(seq_output * neg_items_emb, dim=-1)
          loss = self.loss_fct(pos_score, neg_score)
          return loss + self.reg_loss(user_embedding, pos_items_emb)
      else:  # self.loss_type = 'CE'
          test_item_emb = self.item_embedding.weight
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          loss = self.loss_fct(logits, pos_items)
          return loss + self.reg_loss(user_embedding, pos_items_emb)
  
file_path: RecBole/recbole/model/sequential_recommender/shan.py
imports: import numpy
  import torch
  import torch.nn
  from torch.nn.init import normal_, uniform_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 5 for calling the SHAN obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown, unknown