FDSA¶

Reference:: Tingting Zhang et al. “Feature-level Deeper Self-Attention Network for Sequential Recommendation.” In IJCAI 2019

class recbole.model.sequential_recommender.fdsa.FDSA(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

FDSA is similar with the GRU4RecF implemented in RecBole, which uses two different Transformer encoders to encode items and features respectively and concatenates the two subparts’ outputs as the final output.

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]¶: Generate left-to-right uni-directional attention mask for multi-head attention.

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¶