FNN¶

Reference:: Weinan Zhang1 et al. “Deep Learning over Multi-field Categorical Data” in ECIR 2016

class recbole.model.context_aware_recommender.fnn.FNN(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.ContextRecommender

FNN which also called DNN is a basic version of CTR model that use mlp from field features to predict score.

Note

Based on the experiments in the paper above, This implementation incorporate Dropout instead of L2 normalization to relieve over-fitting. Our implementation of FNN is a basic version without pretrain support. If you want to pretrain the feature embedding as the original paper, we suggest you to construct a advanced FNN model and train it in two-stage process with our FM model.

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(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 Module instance 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