MultiDAE¶

Reference:: Dawen Liang et al. “Variational Autoencoders for Collaborative Filtering.” in WWW 2018.

class recbole.model.general_recommender.multidae.MultiDAE(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.GeneralRecommender

MultiDAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.

We implement the the MultiDAE 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(rating_matrix)[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_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 = 2¶

mlp_layers(layer_dims)[source]¶

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¶