hopwise.model.sequential_recommender.hgn¶

Classes¶

Module Contents¶

class hopwise.model.sequential_recommender.hgn.HGN(config, dataset)¶

Bases: hopwise.model.abstract_recommender.SequentialRecommender

HGN sets feature gating and instance gating to get the important feature and item for predicting the next item

n_user¶

device¶

embedding_size¶

reg_weight¶

pool_type¶

item_embedding¶

user_embedding¶

w1¶

w2¶

b¶

w3¶

w4¶

item_embedding_for_prediction¶

sigmoid¶

loss_type¶

reg_loss(user_embedding, item_embedding, seq_item_embedding)¶

_init_weights(module)¶

feature_gating(seq_item_embedding, user_embedding)¶

Choose the features that will be sent to the next stage(more important feature, more focus)

instance_gating(user_item, user_embedding)¶

Choose the last click items that will influence the prediction( more important more chance to get attention)

forward(seq_item, user)¶

calculate_loss(interaction)¶

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)¶

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)¶

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