DCN V2¶
- Reference:
Ruoxi Wang at al. “Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems.” in WWW 2021.
- Reference code:
- class recbole.model.context_aware_recommender.dcnv2.DCNV2(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.ContextRecommenderDCNV2 improves the cross network by extending the original weight vector to a matrix, significantly improves the expressiveness of DCN. It also introduces the MoE and low rank techniques to reduce time cost.
- 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
- cross_network(x_0)[source]¶
Cross network is composed of cross layers, with each layer having the following formula.
\[x_{l+1} = x_0 \odot (W_l x_l + b_l) + x_l\]\(x_l\), \(x_{l+1}\) 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
- cross_network_mix(x_0)[source]¶
Cross network part of DCN-mix, which add MoE and nonlinear transformation in low-rank space.
\[x_{l+1} = \sum_{i=1}^K G_i(x_l)E_i(x_l)+x_l\]\[E_i(x_l) = x_0 \odot (U_l^i \dot g(C_l^i \dot g(V_L^{iT} x_l)) + b_l)\]\(E_i\) and \(G_i\) represents the expert and gatings respectively, \(U_l\), \(C_l\), \(V_l\) stand for low-rank decomposition of weight matrix, \(g\) is the nonlinear activation function.
- Parameters
x_0 (torch.Tensor) – Embedding vectors of all features, input of cross network.
- Returns
output of mixed cross network, [batch_size, num_feature_field * embedding_size]
- Return type
torch.Tensor
- 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.
- 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
- training: bool¶