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.SequentialRecommenderFDSA 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
Moduleinstance 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¶