AFM

Reference:

Jun Xiao et al. “Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks” in IJCAI 2017.

class recbole.model.context_aware_recommender.afm.AFM(config, dataset)

Bases: ContextRecommender

AFM is a attention based FM model that predict the final score with the attention of input feature.

afm_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

build_cross(feat_emb)

Build the cross feature columns of feature columns

Parameters:

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

Returns:

• torch.FloatTensor: Left part of the cross feature. shape of [batch_size, num_pairs, emb_dim].

• torch.FloatTensor: Right part of the cross feature. shape of [batch_size, num_pairs, emb_dim].

Return type:

tuple

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

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.

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