LightGCN¶
- Reference:
Xiangnan He et al. “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation.” in SIGIR 2020.
- Reference code:
- class recbole.model.general_recommender.lightgcn.LightGCN(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.GeneralRecommenderLightGCN is a GCN-based recommender model.
LightGCN includes only the most essential component in GCN — neighborhood aggregation — for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding.
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()[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.
- 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_ego_embeddings()[source]¶
Get the embedding of users and items and combine to an embedding matrix.
- Returns
Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim]
- 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.
- 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
- training: bool¶