NPE¶

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

class recbole.model.sequential_recommender.npe.NPE(config, dataset)[source]¶

Bases: recbole.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

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(seq_item, user)[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

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¶