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]¶ Bases:
recbole.model.abstract_recommender.ContextRecommenderAutoInt is a novel CTR prediction model based on self-attention mechanism, which can automatically learn high-order feature interactions in an explicit fashion.
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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
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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
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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.
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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
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