# LightGCN¶

Reference:

Xiangnan He et al. “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation.” in SIGIR 2020.

Reference code:

https://github.com/kuandeng/LightGCN

class recbole.model.general_recommender.lightgcn.LightGCN(config, dataset)[source]

LightGCN 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 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_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 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