DCN

Reference:

Ruoxi Wang at al. “Deep & Cross Network for Ad Click Predictions.” in ADKDD 2017.

Reference code:

https://github.com/shenweichen/DeepCTR-Torch

class recbole.model.context_aware_recommender.dcn.DCN(config, dataset)

Bases: ContextRecommender

Deep & Cross Network replaces the wide part in Wide&Deep with cross network, automatically construct limited high-degree cross features, and learns the corresponding weights.

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

cross_network(x_0)

Cross network is composed of cross layers, with each layer having the following formula.

\[x_{l+1} = x_0 {x_l^T} w_l + b_l + x_l\]

\(x_l\), \(x_{l+1}\) are column vectors denoting the outputs from the l -th and (l + 1)-th cross layers, respectively. \(w_l\), \(b_l\) are the weight and bias parameters of the l -th layer.

Parameters:

x_0 (torch.Tensor) – Embedding vectors of all features, input of cross network.

Returns:

output of cross network, [batch_size, num_feature_field * embedding_size]

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