CDAE¶
- Reference:
Yao Wu et al., Collaborative denoising auto-encoders for top-n recommender systems. In WSDM 2016.
- Reference code:
- class recbole.model.general_recommender.cdae.CDAE(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.GeneralRecommenderCollaborative Denoising Auto-Encoder (CDAE) is a recommendation model for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. We implement the the CDAE model with only user dataloader.
- 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(x_items, x_users)[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
- get_rating_matrix(user)[source]¶
Get a batch of user’s feature with the user’s id and history interaction matrix.
- Parameters
user (torch.LongTensor) – The input tensor that contains user’s id, shape: [batch_size, ]
- Returns
The user’s feature of a batch of user, shape: [batch_size, n_items]
- Return type
torch.FloatTensor
- input_type = 1¶
- 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¶