hopwise.model.sequential_recommender.gru4reckg¶

Classes¶

It is an extension of GRU4Rec, which concatenates item and its corresponding

Module Contents¶

class hopwise.model.sequential_recommender.gru4reckg.GRU4RecKG(config, dataset)[source]¶

Bases: hopwise.model.abstract_recommender.SequentialRecommender

It is an extension of GRU4Rec, which concatenates item and its corresponding pre-trained knowledge graph embedding feature as the input.

entity_embedding_matrix¶

embedding_size¶

hidden_size¶

num_layers¶

dropout¶

freeze_kg¶

loss_type¶

item_embedding¶

entity_embedding¶

item_emb_dropout¶

entity_emb_dropout¶

item_gru_layers¶

entity_gru_layers¶

dense_layer¶

forward(item_seq, item_seq_len)[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