FiGNN

Reference:

Li, Zekun, et al. “Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction” in CIKM 2019.

Reference code:

• https://github.com/CRIPAC-DIG/GraphCTR

• https://github.com/xue-pai/FuxiCTR

class recbole.model.context_aware_recommender.fignn.FiGNN(config, dataset)

Bases: recbole.model.abstract_recommender.ContextRecommender

FiGNN is a CTR prediction model based on GGNN, which can model sophisticated interactions among feature fields on the graph-structured features.

calculate_loss(interaction)

Calculate the training loss for a batch data.

Parameters

interaction (Interaction) – Interaction class of the batch.

Returns

Training loss, shape: []

Return type

torch.Tensor

fignn_layer(in_feature)

forward(interaction)

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 = 1

predict(interaction)

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

training: bool

class recbole.model.context_aware_recommender.fignn.GraphLayer(num_fields, embedding_size)

Bases: torch.nn.modules.module.Module

The implementations of the GraphLayer part and the Attentional Edge Weights part are adapted from https://github.com/xue-pai/FuxiCTR.

forward(g, h)

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