ConvNCF¶
 Reference:
Xiangnan He et al. “Outer Productbased Neural Collaborative Filtering.” in IJCAI 2018.
 Reference code:

class
recbole.model.general_recommender.convncf.
ConvNCF
(config, dataset)[source]¶ Bases:
recbole.model.abstract_recommender.GeneralRecommender
ConvNCF is a a new neural network framework for collaborative filtering based on NCF. It uses an outer product operation above the embedding layer, which results in a semanticrich interaction map that encodes pairwise correlations between embedding dimensions. We carefully design the data interface and use sparse tensor to train and test efficiently. We implement the model following the original author with a pairwise training mode.

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

forward
(user, item)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

input_type
= 2¶

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
()[source]¶ Calculate the L2 normalization loss of model parameters. Including embedding matrices and weight matrices of model.
 Returns
The L2 Loss tensor. shape of [1,]
 Return type
loss(torch.FloatTensor)

training
: bool¶


class
recbole.model.general_recommender.convncf.
ConvNCFBPRLoss
[source]¶ Bases:
torch.nn.modules.module.Module
ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
 Shape:
Pos_score: (N)
Neg_score: (N), same shape as the Pos_score
Output: scalar.
Examples:
>>> loss = ConvNCFBPRLoss() >>> pos_score = torch.randn(3, requires_grad=True) >>> neg_score = torch.randn(3, requires_grad=True) >>> output = loss(pos_score, neg_score) >>> output.backward()

forward
(pos_score, neg_score)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training
: bool¶