DCN¶
- Reference:
Ruoxi Wang at al. “Deep & Cross Network for Ad Click Predictions.” in ADKDD 2017.
- Reference code:
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class
recbole.model.context_aware_recommender.dcn.DCN(config, dataset)[source]¶ Bases:
recbole.model.abstract_recommender.ContextRecommenderDeep & Cross Network replaces the wide part in Wide&Deep with cross network, automatically construct limited high-degree cross features, and learns the corresponding weights.
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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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cross_network(x_0)[source]¶ 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
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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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