hopwise.model.sequential_recommender.npe¶

Reference:: ThaiBinh Nguyen, et al. “NPE: Neural Personalized Embedding for Collaborative Filtering” in IJCAI 2018.
Reference code:: https://github.com/wubinzzu/NeuRec

Classes¶

NPE

models a user’s click to an item in two terms: the personal preference of the user for the item,

Module Contents¶

class hopwise.model.sequential_recommender.npe.NPE(config, dataset)¶

Bases: hopwise.model.abstract_recommender.SequentialRecommender

models a user’s click to an item in two terms: the personal preference of the user for the item, and the relationships between this item and other items clicked by the user

n_user¶

device¶

embedding_size¶

dropout_prob¶

user_embedding¶

item_embedding¶

embedding_seq_item¶

relu¶

dropout¶

loss_type¶

_init_weights(module)¶

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