GCMC¶
 Reference:
van den Berg et al. “Graph Convolutional Matrix Completion.” in SIGKDD 2018.
 Reference code:

class
recbole.model.general_recommender.gcmc.
BiDecoder
(input_dim, output_dim, drop_prob, device, num_weights=3, act=<function BiDecoder.<lambda>>)[source]¶ Bases:
torch.nn.modules.module.Module
Bilinear decoder BiDecoder takes pairs of node embeddings and predicts respective entries in the adjacency matrix.

forward
(u_inputs, i_inputs, users, items=None)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.


class
recbole.model.general_recommender.gcmc.
GCMC
(config, dataset)[source]¶ Bases:
recbole.model.abstract_recommender.GeneralRecommender
GCMC is a model that incorporate graph autoencoders for recommendation.
Graph autoencoders are comprised of:
1) a graph encoder model \(Z = f(X; A)\), which take as input an \(N \times D\) feature matrix X and a graph adjacency matrix A, and produce an \(N \times E\) node embedding matrix \(Z = [z_1^T,..., z_N^T ]^T\);
2) a pairwise decoder model \(\hat A = g(Z)\), which takes pairs of node embeddings \((z_i, z_j)\) and predicts respective entries \(\hat A_{ij}\) in the adjacency matrix.
Note that \(N\) denotes the number of nodes, \(D\) the number of input features, and \(E\) the embedding size.
We implement the model following the original author with a pairwise training mode.

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

forward
(user_X, item_X, user, item)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

full_sort_predict
(interaction)[source]¶ full sort prediction function. Given users, calculate the scores between users and all candidate items.
 Parameters
interaction (Interaction) – Interaction class of the batch.
 Returns
Predicted scores for given users and all candidate items, shape: [n_batch_users * n_candidate_items]
 Return type
torch.Tensor

get_norm_adj_mat
()[source]¶ Get the normalized interaction matrix of users and items.
Construct the square matrix from the training data and normalize it using the laplace matrix.
\[A_{hat} = D^{0.5} \times A \times D^{0.5}\] Returns
Sparse tensor of the normalized interaction matrix.

get_sparse_eye_mat
(num)[source]¶ Get the normalized sparse eye matrix.
Construct the sparse eye matrix as node feature.
 Parameters
num – the number of rows
 Returns
Sparse tensor of the normalized interaction matrix.

input_type
= 2¶

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


class
recbole.model.general_recommender.gcmc.
GcEncoder
(accum, num_user, num_item, support, input_dim, gcn_output_dim, dense_output_dim, drop_prob, device, sparse_feature=True, act_dense=<function GcEncoder.<lambda>>, share_user_item_weights=True, bias=False)[source]¶ Bases:
torch.nn.modules.module.Module
Graph Convolutional Encoder GcEncoder take as input an \(N imes D\) feature matrix \(X\) and a graph adjacency matrix \(A\), and produce an \(N imes E\) node embedding matrix; Note that \(N\) denotes the number of nodes, \(D\) the number of input features, and \(E\) the embedding size.

forward
(user_X, item_X)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.


class
recbole.model.general_recommender.gcmc.
SparseDropout
(p=0.5)[source]¶ Bases:
torch.nn.modules.module.Module
This is a Module that execute Dropout on Pytorch sparse tensor.

forward
(x)[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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.


recbole.model.general_recommender.gcmc.
orthogonal
(shape, scale=1.1)[source]¶ Initialization function for weights in class GCMC. From Lasagne. Reference: Saxe et al., http://arxiv.org/abs/1312.6120