hopwise.model.general_recommender.ease¶
- Reference:
Harald Steck. “Embarrassingly Shallow Autoencoders for Sparse Data” in WWW 2019.
Classes¶
EASE is a linear model for collaborative filtering, which combines the |
Module Contents¶
- class hopwise.model.general_recommender.ease.EASE(config, dataset)¶
Bases:
hopwise.model.abstract_recommender.GeneralRecommenderEASE is a linear model for collaborative filtering, which combines the strengths of auto-encoders and neighborhood-based approaches.
- input_type¶
- type¶
- dummy_param¶
- item_similarity¶
- interaction_matrix¶
- other_parameter_name = ['interaction_matrix', 'item_similarity']¶
- device¶
- forward()¶
- calculate_loss(interaction)¶
Calculate the training loss for a batch data.
- Parameters:
interaction (Interaction) – Interaction class of the batch.
- Returns:
Training loss, shape: []
- Return type:
torch.Tensor
- predict(interaction)¶
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
- full_sort_predict(interaction)¶
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