LightSANs¶

Reference:: Xin-Yan Fan et al. “Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation.” in SIGIR 2021.
Reference:: https://github.com/BELIEVEfxy/LightSANs

class recbole.model.sequential_recommender.lightsans.LightSANs(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

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

embedding_layer(item_seq)[source]¶

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¶