SASRec¶

Reference:: Wang-Cheng Kang et al. “Self-Attentive Sequential Recommendation.” in ICDM 2018.
Reference:: https://github.com/kang205/SASRec

class recbole.model.sequential_recommender.sasrec.SASRec(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

SASRec is the first sequential recommender based on self-attentive mechanism.

Note

In the author’s implementation, the Point-Wise Feed-Forward Network (PFFN) is implemented by CNN with 1x1 kernel. In this implementation, we follows the original BERT implementation using Fully Connected Layer to implement the PFFN.

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(item_seq, item_seq_len)[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¶