hopwise.model.general_recommender.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

Classes¶

LightGCN

LightGCN is a GCN-based recommender model.

Module Contents¶

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

Bases: hopwise.model.abstract_recommender.GeneralRecommender

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.

input_type¶

latent_dim¶

n_layers¶

reg_weight¶

require_pow¶

user_embedding¶

item_embedding¶

mf_loss¶

reg_loss¶

restore_user_e = None¶

restore_item_e = None¶

norm_adj_matrix¶

other_parameter_name = ['restore_user_e', 'restore_item_e']¶

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]

forward()[source]¶

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

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

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