# AutoInt¶

Reference:

Weiping Song et al. “AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks” in CIKM 2018.

class recbole.model.context_aware_recommender.autoint.AutoInt(config, dataset)[source]

AutoInt is a novel CTR prediction model based on self-attention mechanism, which can automatically learn high-order feature interactions in an explicit fashion.

autoint_layer(infeature)[source]

Get the attention-based feature interaction score

Parameters

infeature (torch.FloatTensor) – input feature embedding tensor. shape of[batch_size,field_size,embed_dim].

Returns

Result of score. shape of [batch_size,1] .

Return type

torch.FloatTensor

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