ENMF¶

Reference:: Chong Chen et al. “Efficient Neural Matrix Factorization without Sampling for Recommendation.” in TOIS 2020.
Reference code:: https://github.com/chenchongthu/ENMF

class recbole.model.general_recommender.enmf.ENMF(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.GeneralRecommender

ENMF is an efficient non-sampling model for general recommendation. In order to run non-sampling model, please set the neg_sampling parameter as None .

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(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

input_type = 1¶

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

reg_loss()[source]¶

calculate the reg loss for embedding layers and mlp layers

Returns: reg loss
Return type: torch.Tensor

training: bool¶