hopwise.model.context_aware_recommender.autoint¶

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

Classes¶

AutoInt

AutoInt is a novel CTR prediction model based on self-attention mechanism,

Module Contents¶

class hopwise.model.context_aware_recommender.autoint.AutoInt(config, dataset)¶

Bases: hopwise.model.abstract_recommender.ContextRecommender

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

attention_size¶

dropout_probs¶

n_layers¶

num_heads¶

mlp_hidden_size¶

has_residual¶

att_embedding¶

embed_output_dim¶

atten_output_dim¶

mlp_layers¶

self_attns¶

attn_fc¶

deep_predict_layer¶

dropout_layer¶

sigmoid¶

loss¶

_init_weights(module)¶

autoint_layer(infeature)¶

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

forward(interaction)¶

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

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