xDeepFM¶
- Reference:
Jianxun Lian at al. “xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems.” in SIGKDD 2018.
- Reference code:
- class recbole.model.context_aware_recommender.xdeepfm.xDeepFM(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.ContextRecommenderxDeepFM combines a CIN (Compressed Interaction Network) with a classical DNN. The model is able to learn certain bounded-degree feature interactions explicitly; Besides, it can also learn arbitrary low- and high-order feature interactions implicitly.
- calculate_loss(interaction)[source]¶
Calculate the training loss for a batch data.
- Parameters
interaction (Interaction) – Interaction class of the batch.
- Returns
Training loss, shape: []
- Return type
torch.Tensor
- calculate_reg_loss()[source]¶
Calculate the final L2 normalization loss of model parameters. Including weight matrices of mlp layers, linear layer and convolutional layers.
- Returns
The L2 Loss tensor. shape of [1,]
- Return type
loss(torch.FloatTensor)
- compressed_interaction_network(input_features, activation='ReLU')[source]¶
For k-th CIN layer, the output \(X_k\) is calculated via
\[x_{h,*}^{k} = \sum_{i=1}^{H_k-1} \sum_{j=1}^{m}W_{i,j}^{k,h}(X_{i,*}^{k-1} \circ x_{j,*}^0)\]\(H_k\) donates the number of feature vectors in the k-th layer, \(1 \le h \le H_k\). \(\circ\) donates the Hadamard product.
And Then, We apply sum pooling on each feature map of the hidden layer. Finally, All pooling vectors from hidden layers are concatenated.
- Parameters
input_features (torch.Tensor) – [batch_size, field_num, embed_dim]. Embedding vectors of all features.
activation (str) – name of activation function.
- Returns
[batch_size, num_feature_field * embedding_size]. output of CIN layer.
- Return type
torch.Tensor
- forward(interaction)[source]¶
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- predict(interaction)[source]¶
Predict the scores between users and items.
- Parameters
interaction (Interaction) – Interaction class of the batch.
- Returns
Predicted scores for given users and items, shape: [batch_size]
- Return type
torch.Tensor
- reg_loss(parameters)[source]¶
Calculate the L2 normalization loss of parameters in a certain layer.
- Returns
The L2 Loss tensor. shape of [1,]
- Return type
loss(torch.FloatTensor)
- training: bool¶