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"]
docstr: fusing the long term purpose with the short-term preference
docstr_tok: ["fusing","the","long","term","purpose","with","the","short-term","preference"]
code: def long_and_short_term_attention_based_pooling_layer(
      self, long_short_item_embedding, user_embedding, mask=None
  ):
      """
  
      fusing the long term purpose with the short-term preference
      """
      long_short_item_embedding_value = long_short_item_embedding
  
      long_short_item_embedding = self.relu(
          self.long_short_w(long_short_item_embedding)
          + self.long_short_b
      )
      long_short_item_embedding = torch.matmul(
          long_short_item_embedding, user_embedding.unsqueeze(2)
      ).squeeze(-1)
      # batch_size * seq_len
      if mask is not None:
          long_short_item_embedding.masked_fill_(mask, -1e9)
      long_short_item_embedding = nn.Softmax(dim=-1)(
          long_short_item_embedding
      )
      long_short_item_embedding = torch.mul(
          long_short_item_embedding_value,
          long_short_item_embedding.unsqueeze(2),
      ).sum(dim=1)
  
      return long_short_item_embedding
  
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 8 for calling the SHAN obj's underlying member method code with example usage: obj.long_and_short_term_attention_based_pooling_layer(long_short_item_embedding, user_embedding, mask) and returns: long_short_item_embedding