NARM

Reference:

Jing Li et al. “Neural Attentive Session-based Recommendation.” in CIKM 2017.

Reference code:

https://github.com/Wang-Shuo/Neural-Attentive-Session-Based-Recommendation-PyTorch

class recbole.model.sequential_recommender.narm.NARM(config, dataset)

Bases: SequentialRecommender

NARM explores a hybrid encoder with an attention mechanism to model the user’s sequential behavior, and capture the user’s main purpose in the current session.

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

forward(item_seq, item_seq_len)

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)

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)

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