class_docstr: EulerNet is a context-based recommendation model.
  It can adaptively learn the arbitrary-order feature interactions in a complex vector space
  by conducting space mapping according to Euler's formula. Meanwhile, it can jointly capture
  the explicit and implicit feature interactions in a unified model architecture.
class_docstr_tok: ["EulerNet","is","a","context-based","recommendation","model",".","It","can","adaptively","learn","the","arbitrary-order","feature","interactions","in","a","complex","vector","space","by","conducting","space","mapping","according","to","Euler","'s","formula",".","Meanwhile",",","it","can","jointly","capture","the","explicit","and","implicit","feature","interactions","in","a","unified","model","architecture","."]
code: def __init__(self, config, dataset):
      super(EulerNet, self).__init__(config, dataset)
      field_num = self.field_num = self.num_feature_field
      shape_list = [config.embedding_size * field_num] + [
          num_neurons * config.embedding_size
          for num_neurons in config.order_list
      ]
  
      interaction_shapes = []
      for inshape, outshape in zip(shape_list[:-1], shape_list[1:]):
          interaction_shapes.append(
              EulerInteractionLayer(config, inshape, outshape)
          )
  
      self.Euler_interaction_layers = nn.Sequential(*interaction_shapes)
      self.mu = nn.Parameter(torch.ones(1, field_num, 1))
      self.reg = nn.Linear(shape_list[-1], 1)
      self.reg_weight = config.reg_weight
      nn.init.normal_(self.reg.weight, mean=0, std=0.01)
      self.sigmoid = nn.Sigmoid()
      self.reg_loss = RegLoss()
      self.loss = nn.BCEWithLogitsLoss()
      self.apply(self._init_other_weights)
  
file_path: RecBole/recbole/model/context_aware_recommender/eulernet.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.loss import RegLoss
label_desc: Use image node_id 1 to create a new EulerNet object from inherited base classes: ContextRecommender with example: obj = EulerNet(config, dataset)