# 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]

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 implmentation 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

get_attention_mask(item_seq)[source]

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